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<meta name="author" content="Hanako Ohmura, Masahiro Zenkyo, Masaki Hata, and Tetsuya Matsubayashi" />

<meta name="date" content="2026-01-26" />

<title>Replication Codes for ‘From Olympic Gold to Government Approval?: Limited Evidence of Attribution Error from the 2024 Paris Games’</title>

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) 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}
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font-family: 'Lato';
font-style: italic;
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) 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e,i;this.previousFilter||t.clientX===this.lastMousePosition.x&&t.clientY===this.lastMousePosition.y||(this.lastMousePosition={x:t.clientX,y:t.clientY},e=V(t.target).closest(".ui-menu-item"),i=V(t.currentTarget),e[0]===i[0]&&(i.is(".ui-state-active")||(this._removeClass(i.siblings().children(".ui-state-active"),null,"ui-state-active"),this.focus(t,i))))},_destroy:function(){var t=this.element.find(".ui-menu-item").removeAttr("role aria-disabled").children(".ui-menu-item-wrapper").removeUniqueId().removeAttr("tabIndex role aria-haspopup");this.element.removeAttr("aria-activedescendant").find(".ui-menu").addBack().removeAttr("role aria-labelledby aria-expanded aria-hidden aria-disabled tabIndex").removeUniqueId().show(),t.children().each(function(){var t=V(this);t.data("ui-menu-submenu-caret")&&t.remove()})},_keydown:function(t){var e,i,s,n=!0;switch(t.keyCode){case V.ui.keyCode.PAGE_UP:this.previousPage(t);break;case V.ui.keyCode.PAGE_DOWN:this.nextPage(t);break;case V.ui.keyCode.HOME:this._move("first","first",t);break;case V.ui.keyCode.END:this._move("last","last",t);break;case V.ui.keyCode.UP:this.previous(t);break;case V.ui.keyCode.DOWN:this.next(t);break;case V.ui.keyCode.LEFT:this.collapse(t);break;case V.ui.keyCode.RIGHT:this.active&&!this.active.is(".ui-state-disabled")&&this.expand(t);break;case V.ui.keyCode.ENTER:case V.ui.keyCode.SPACE:this._activate(t);break;case V.ui.keyCode.ESCAPE:this.collapse(t);break;default:e=this.previousFilter||"",s=n=!1,i=96<=t.keyCode&&t.keyCode<=105?(t.keyCode-96).toString():String.fromCharCode(t.keyCode),clearTimeout(this.filterTimer),i===e?s=!0:i=e+i,e=this._filterMenuItems(i),(e=s&&-1!==e.index(this.active.next())?this.active.nextAll(".ui-menu-item"):e).length||(i=String.fromCharCode(t.keyCode),e=this._filterMenuItems(i)),e.length?(this.focus(t,e),this.previousFilter=i,this.filterTimer=this._delay(function(){delete this.previousFilter},1e3)):delete this.previousFilter}n&&t.preventDefault()},_activate:function(t){this.active&&!this.active.is(".ui-state-disabled")&&(this.active.children("[aria-haspopup='true']").length?this.expand(t):this.select(t))},refresh:function(){var t,e,s=this,n=this.options.icons.submenu,i=this.element.find(this.options.menus);this._toggleClass("ui-menu-icons",null,!!this.element.find(".ui-icon").length),e=i.filter(":not(.ui-menu)").hide().attr({role:this.options.role,"aria-hidden":"true","aria-expanded":"false"}).each(function(){var t=V(this),e=t.prev(),i=V("<span>").data("ui-menu-submenu-caret",!0);s._addClass(i,"ui-menu-icon","ui-icon "+n),e.attr("aria-haspopup","true").prepend(i),t.attr("aria-labelledby",e.attr("id"))}),this._addClass(e,"ui-menu","ui-widget ui-widget-content ui-front"),(t=i.add(this.element).find(this.options.items)).not(".ui-menu-item").each(function(){var t=V(this);s._isDivider(t)&&s._addClass(t,"ui-menu-divider","ui-widget-content")}),i=(e=t.not(".ui-menu-item, 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i;this.blur(t,t&&"focus"===t.type),this._scrollIntoView(e),this.active=e.first(),i=this.active.children(".ui-menu-item-wrapper"),this._addClass(i,null,"ui-state-active"),this.options.role&&this.element.attr("aria-activedescendant",i.attr("id")),i=this.active.parent().closest(".ui-menu-item").children(".ui-menu-item-wrapper"),this._addClass(i,null,"ui-state-active"),t&&"keydown"===t.type?this._close():this.timer=this._delay(function(){this._close()},this.delay),(i=e.children(".ui-menu")).length&&t&&/^mouse/.test(t.type)&&this._startOpening(i),this.activeMenu=e.parent(),this._trigger("focus",t,{item:e})},_scrollIntoView:function(t){var e,i,s;this._hasScroll()&&(i=parseFloat(V.css(this.activeMenu[0],"borderTopWidth"))||0,s=parseFloat(V.css(this.activeMenu[0],"paddingTop"))||0,e=t.offset().top-this.activeMenu.offset().top-i-s,i=this.activeMenu.scrollTop(),s=this.activeMenu.height(),t=t.outerHeight(),e<0?this.activeMenu.scrollTop(i+e):s<e+t&&this.activeMenu.scrollTop(i+e-s+t))},blur:function(t,e){e||clearTimeout(this.timer),this.active&&(this._removeClass(this.active.children(".ui-menu-item-wrapper"),null,"ui-state-active"),this._trigger("blur",t,{item:this.active}),this.active=null)},_startOpening:function(t){clearTimeout(this.timer),"true"===t.attr("aria-hidden")&&(this.timer=this._delay(function(){this._close(),this._open(t)},this.delay))},_open:function(t){var e=V.extend({of:this.active},this.options.position);clearTimeout(this.timer),this.element.find(".ui-menu").not(t.parents(".ui-menu")).hide().attr("aria-hidden","true"),t.show().removeAttr("aria-hidden").attr("aria-expanded","true").position(e)},collapseAll:function(e,i){clearTimeout(this.timer),this.timer=this._delay(function(){var t=i?this.element:V(e&&e.target).closest(this.element.find(".ui-menu"));t.length||(t=this.element),this._close(t),this.blur(e),this._removeClass(t.find(".ui-state-active"),null,"ui-state-active"),this.activeMenu=t},i?0:this.delay)},_close:function(t){(t=t||(this.active?this.active.parent():this.element)).find(".ui-menu").hide().attr("aria-hidden","true").attr("aria-expanded","false")},_closeOnDocumentClick:function(t){return!V(t.target).closest(".ui-menu").length},_isDivider:function(t){return!/[^\-\u2014\u2013\s]/.test(t.text())},collapse:function(t){var e=this.active&&this.active.parent().closest(".ui-menu-item",this.element);e&&e.length&&(this._close(),this.focus(t,e))},expand:function(t){var e=this.active&&this._menuItems(this.active.children(".ui-menu")).first();e&&e.length&&(this._open(e.parent()),this._delay(function(){this.focus(t,e)}))},next:function(t){this._move("next","first",t)},previous:function(t){this._move("prev","last",t)},isFirstItem:function(){return this.active&&!this.active.prevAll(".ui-menu-item").length},isLastItem:function(){return this.active&&!this.active.nextAll(".ui-menu-item").length},_menuItems:function(t){return(t||this.element).find(this.options.items).filter(".ui-menu-item")},_move:function(t,e,i){var s;(s=this.active?"first"===t||"last"===t?this.active["first"===t?"prevAll":"nextAll"](".ui-menu-item").last():this.active[t+"All"](".ui-menu-item").first():s)&&s.length&&this.active||(s=this._menuItems(this.activeMenu)[e]()),this.focus(i,s)},nextPage:function(t){var e,i,s;this.active?this.isLastItem()||(this._hasScroll()?(i=this.active.offset().top,s=this.element.innerHeight(),0===V.fn.jquery.indexOf("3.2.")&&(s+=this.element[0].offsetHeight-this.element.outerHeight()),this.active.nextAll(".ui-menu-item").each(function(){return(e=V(this)).offset().top-i-s<0}),this.focus(t,e)):this.focus(t,this._menuItems(this.activeMenu)[this.active?"last":"first"]())):this.next(t)},previousPage:function(t){var e,i,s;this.active?this.isFirstItem()||(this._hasScroll()?(i=this.active.offset().top,s=this.element.innerHeight(),0===V.fn.jquery.indexOf("3.2.")&&(s+=this.element[0].offsetHeight-this.element.outerHeight()),this.active.prevAll(".ui-menu-item").each(function(){return 0<(e=V(this)).offset().top-i+s}),this.focus(t,e)):this.focus(t,this._menuItems(this.activeMenu).first())):this.next(t)},_hasScroll:function(){return this.element.outerHeight()<this.element.prop("scrollHeight")},select:function(t){this.active=this.active||V(t.target).closest(".ui-menu-item");var e={item:this.active};this.active.has(".ui-menu").length||this.collapseAll(t,!0),this._trigger("select",t,e)},_filterMenuItems:function(t){var t=t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&"),e=new RegExp("^"+t,"i");return this.activeMenu.find(this.options.items).filter(".ui-menu-item").filter(function(){return e.test(String.prototype.trim.call(V(this).children(".ui-menu-item-wrapper").text()))})}});V.widget("ui.autocomplete",{version:"1.13.2",defaultElement:"<input>",options:{appendTo:null,autoFocus:!1,delay:300,minLength:1,position:{my:"left top",at:"left bottom",collision:"none"},source:null,change:null,close:null,focus:null,open:null,response:null,search:null,select:null},requestIndex:0,pending:0,liveRegionTimer:null,_create:function(){var i,s,n,t=this.element[0].nodeName.toLowerCase(),e="textarea"===t,t="input"===t;this.isMultiLine=e||!t&&this._isContentEditable(this.element),this.valueMethod=this.element[e||t?"val":"text"],this.isNewMenu=!0,this._addClass("ui-autocomplete-input"),this.element.attr("autocomplete","off"),this._on(this.element,{keydown:function(t){if(this.element.prop("readOnly"))s=n=i=!0;else{s=n=i=!1;var e=V.ui.keyCode;switch(t.keyCode){case e.PAGE_UP:i=!0,this._move("previousPage",t);break;case e.PAGE_DOWN:i=!0,this._move("nextPage",t);break;case e.UP:i=!0,this._keyEvent("previous",t);break;case e.DOWN:i=!0,this._keyEvent("next",t);break;case e.ENTER:this.menu.active&&(i=!0,t.preventDefault(),this.menu.select(t));break;case e.TAB:this.menu.active&&this.menu.select(t);break;case e.ESCAPE:this.menu.element.is(":visible")&&(this.isMultiLine||this._value(this.term),this.close(t),t.preventDefault());break;default:s=!0,this._searchTimeout(t)}}},keypress:function(t){if(i)return i=!1,void(this.isMultiLine&&!this.menu.element.is(":visible")||t.preventDefault());if(!s){var e=V.ui.keyCode;switch(t.keyCode){case e.PAGE_UP:this._move("previousPage",t);break;case e.PAGE_DOWN:this._move("nextPage",t);break;case e.UP:this._keyEvent("previous",t);break;case e.DOWN:this._keyEvent("next",t)}}},input:function(t){if(n)return n=!1,void t.preventDefault();this._searchTimeout(t)},focus:function(){this.selectedItem=null,this.previous=this._value()},blur:function(t){clearTimeout(this.searching),this.close(t),this._change(t)}}),this._initSource(),this.menu=V("<ul>").appendTo(this._appendTo()).menu({role:null}).hide().attr({unselectable:"on"}).menu("instance"),this._addClass(this.menu.element,"ui-autocomplete","ui-front"),this._on(this.menu.element,{mousedown:function(t){t.preventDefault()},menufocus:function(t,e){var i,s;if(this.isNewMenu&&(this.isNewMenu=!1,t.originalEvent&&/^mouse/.test(t.originalEvent.type)))return this.menu.blur(),void this.document.one("mousemove",function(){V(t.target).trigger(t.originalEvent)});s=e.item.data("ui-autocomplete-item"),!1!==this._trigger("focus",t,{item:s})&&t.originalEvent&&/^key/.test(t.originalEvent.type)&&this._value(s.value),(i=e.item.attr("aria-label")||s.value)&&String.prototype.trim.call(i).length&&(clearTimeout(this.liveRegionTimer),this.liveRegionTimer=this._delay(function(){this.liveRegion.html(V("<div>").text(i))},100))},menuselect:function(t,e){var i=e.item.data("ui-autocomplete-item"),s=this.previous;this.element[0]!==V.ui.safeActiveElement(this.document[0])&&(this.element.trigger("focus"),this.previous=s,this._delay(function(){this.previous=s,this.selectedItem=i})),!1!==this._trigger("select",t,{item:i})&&this._value(i.value),this.term=this._value(),this.close(t),this.selectedItem=i}}),this.liveRegion=V("<div>",{role:"status","aria-live":"assertive","aria-relevant":"additions"}).appendTo(this.document[0].body),this._addClass(this.liveRegion,null,"ui-helper-hidden-accessible"),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_destroy:function(){clearTimeout(this.searching),this.element.removeAttr("autocomplete"),this.menu.element.remove(),this.liveRegion.remove()},_setOption:function(t,e){this._super(t,e),"source"===t&&this._initSource(),"appendTo"===t&&this.menu.element.appendTo(this._appendTo()),"disabled"===t&&e&&this.xhr&&this.xhr.abort()},_isEventTargetInWidget:function(t){var e=this.menu.element[0];return t.target===this.element[0]||t.target===e||V.contains(e,t.target)},_closeOnClickOutside:function(t){this._isEventTargetInWidget(t)||this.close()},_appendTo:function(){var t=this.options.appendTo;return t=!(t=!(t=t&&(t.jquery||t.nodeType?V(t):this.document.find(t).eq(0)))||!t[0]?this.element.closest(".ui-front, dialog"):t).length?this.document[0].body:t},_initSource:function(){var i,s,n=this;Array.isArray(this.options.source)?(i=this.options.source,this.source=function(t,e){e(V.ui.autocomplete.filter(i,t.term))}):"string"==typeof this.options.source?(s=this.options.source,this.source=function(t,e){n.xhr&&n.xhr.abort(),n.xhr=V.ajax({url:s,data:t,dataType:"json",success:function(t){e(t)},error:function(){e([])}})}):this.source=this.options.source},_searchTimeout:function(s){clearTimeout(this.searching),this.searching=this._delay(function(){var t=this.term===this._value(),e=this.menu.element.is(":visible"),i=s.altKey||s.ctrlKey||s.metaKey||s.shiftKey;t&&(e||i)||(this.selectedItem=null,this.search(null,s))},this.options.delay)},search:function(t,e){return t=null!=t?t:this._value(),this.term=this._value(),t.length<this.options.minLength?this.close(e):!1!==this._trigger("search",e)?this._search(t):void 0},_search:function(t){this.pending++,this._addClass("ui-autocomplete-loading"),this.cancelSearch=!1,this.source({term:t},this._response())},_response:function(){var e=++this.requestIndex;return function(t){e===this.requestIndex&&this.__response(t),this.pending--,this.pending||this._removeClass("ui-autocomplete-loading")}.bind(this)},__response:function(t){t=t&&this._normalize(t),this._trigger("response",null,{content:t}),!this.options.disabled&&t&&t.length&&!this.cancelSearch?(this._suggest(t),this._trigger("open")):this._close()},close:function(t){this.cancelSearch=!0,this._close(t)},_close:function(t){this._off(this.document,"mousedown"),this.menu.element.is(":visible")&&(this.menu.element.hide(),this.menu.blur(),this.isNewMenu=!0,this._trigger("close",t))},_change:function(t){this.previous!==this._value()&&this._trigger("change",t,{item:this.selectedItem})},_normalize:function(t){return t.length&&t[0].label&&t[0].value?t:V.map(t,function(t){return"string"==typeof t?{label:t,value:t}:V.extend({},t,{label:t.label||t.value,value:t.value||t.label})})},_suggest:function(t){var e=this.menu.element.empty();this._renderMenu(e,t),this.isNewMenu=!0,this.menu.refresh(),e.show(),this._resizeMenu(),e.position(V.extend({of:this.element},this.options.position)),this.options.autoFocus&&this.menu.next(),this._on(this.document,{mousedown:"_closeOnClickOutside"})},_resizeMenu:function(){var t=this.menu.element;t.outerWidth(Math.max(t.width("").outerWidth()+1,this.element.outerWidth()))},_renderMenu:function(i,t){var s=this;V.each(t,function(t,e){s._renderItemData(i,e)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-autocomplete-item",e)},_renderItem:function(t,e){return V("<li>").append(V("<div>").text(e.label)).appendTo(t)},_move:function(t,e){if(this.menu.element.is(":visible"))return this.menu.isFirstItem()&&/^previous/.test(t)||this.menu.isLastItem()&&/^next/.test(t)?(this.isMultiLine||this._value(this.term),void this.menu.blur()):void this.menu[t](e);this.search(null,e)},widget:function(){return this.menu.element},_value:function(){return this.valueMethod.apply(this.element,arguments)},_keyEvent:function(t,e){this.isMultiLine&&!this.menu.element.is(":visible")||(this._move(t,e),e.preventDefault())},_isContentEditable:function(t){if(!t.length)return!1;var e=t.prop("contentEditable");return"inherit"===e?this._isContentEditable(t.parent()):"true"===e}}),V.extend(V.ui.autocomplete,{escapeRegex:function(t){return t.replace(/[\-\[\]{}()*+?.,\\\^$|#\s]/g,"\\$&")},filter:function(t,e){var i=new RegExp(V.ui.autocomplete.escapeRegex(e),"i");return V.grep(t,function(t){return i.test(t.label||t.value||t)})}}),V.widget("ui.autocomplete",V.ui.autocomplete,{options:{messages:{noResults:"No search results.",results:function(t){return t+(1<t?" results are":" result is")+" available, use up and down arrow keys to navigate."}}},__response:function(t){var e;this._superApply(arguments),this.options.disabled||this.cancelSearch||(e=t&&t.length?this.options.messages.results(t.length):this.options.messages.noResults,clearTimeout(this.liveRegionTimer),this.liveRegionTimer=this._delay(function(){this.liveRegion.html(V("<div>").text(e))},100))}});V.ui.autocomplete;var tt=/ui-corner-([a-z]){2,6}/g;V.widget("ui.controlgroup",{version:"1.13.2",defaultElement:"<div>",options:{direction:"horizontal",disabled:null,onlyVisible:!0,items:{button:"input[type=button], input[type=submit], input[type=reset], button, a",controlgroupLabel:".ui-controlgroup-label",checkboxradio:"input[type='checkbox'], input[type='radio']",selectmenu:"select",spinner:".ui-spinner-input"}},_create:function(){this._enhance()},_enhance:function(){this.element.attr("role","toolbar"),this.refresh()},_destroy:function(){this._callChildMethod("destroy"),this.childWidgets.removeData("ui-controlgroup-data"),this.element.removeAttr("role"),this.options.items.controlgroupLabel&&this.element.find(this.options.items.controlgroupLabel).find(".ui-controlgroup-label-contents").contents().unwrap()},_initWidgets:function(){var o=this,a=[];V.each(this.options.items,function(s,t){var e,n={};if(t)return"controlgroupLabel"===s?((e=o.element.find(t)).each(function(){var t=V(this);t.children(".ui-controlgroup-label-contents").length||t.contents().wrapAll("<span class='ui-controlgroup-label-contents'></span>")}),o._addClass(e,null,"ui-widget ui-widget-content ui-state-default"),void(a=a.concat(e.get()))):void(V.fn[s]&&(n=o["_"+s+"Options"]?o["_"+s+"Options"]("middle"):{classes:{}},o.element.find(t).each(function(){var t=V(this),e=t[s]("instance"),i=V.widget.extend({},n);"button"===s&&t.parent(".ui-spinner").length||((e=e||t[s]()[s]("instance"))&&(i.classes=o._resolveClassesValues(i.classes,e)),t[s](i),i=t[s]("widget"),V.data(i[0],"ui-controlgroup-data",e||t[s]("instance")),a.push(i[0]))})))}),this.childWidgets=V(V.uniqueSort(a)),this._addClass(this.childWidgets,"ui-controlgroup-item")},_callChildMethod:function(e){this.childWidgets.each(function(){var t=V(this).data("ui-controlgroup-data");t&&t[e]&&t[e]()})},_updateCornerClass:function(t,e){e=this._buildSimpleOptions(e,"label").classes.label;this._removeClass(t,null,"ui-corner-top ui-corner-bottom ui-corner-left ui-corner-right ui-corner-all"),this._addClass(t,null,e)},_buildSimpleOptions:function(t,e){var i="vertical"===this.options.direction,s={classes:{}};return s.classes[e]={middle:"",first:"ui-corner-"+(i?"top":"left"),last:"ui-corner-"+(i?"bottom":"right"),only:"ui-corner-all"}[t],s},_spinnerOptions:function(t){t=this._buildSimpleOptions(t,"ui-spinner");return t.classes["ui-spinner-up"]="",t.classes["ui-spinner-down"]="",t},_buttonOptions:function(t){return this._buildSimpleOptions(t,"ui-button")},_checkboxradioOptions:function(t){return this._buildSimpleOptions(t,"ui-checkboxradio-label")},_selectmenuOptions:function(t){var e="vertical"===this.options.direction;return{width:e&&"auto",classes:{middle:{"ui-selectmenu-button-open":"","ui-selectmenu-button-closed":""},first:{"ui-selectmenu-button-open":"ui-corner-"+(e?"top":"tl"),"ui-selectmenu-button-closed":"ui-corner-"+(e?"top":"left")},last:{"ui-selectmenu-button-open":e?"":"ui-corner-tr","ui-selectmenu-button-closed":"ui-corner-"+(e?"bottom":"right")},only:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"}}[t]}},_resolveClassesValues:function(i,s){var n={};return V.each(i,function(t){var e=s.options.classes[t]||"",e=String.prototype.trim.call(e.replace(tt,""));n[t]=(e+" "+i[t]).replace(/\s+/g," ")}),n},_setOption:function(t,e){"direction"===t&&this._removeClass("ui-controlgroup-"+this.options.direction),this._super(t,e),"disabled"!==t?this.refresh():this._callChildMethod(e?"disable":"enable")},refresh:function(){var n,o=this;this._addClass("ui-controlgroup ui-controlgroup-"+this.options.direction),"horizontal"===this.options.direction&&this._addClass(null,"ui-helper-clearfix"),this._initWidgets(),n=this.childWidgets,(n=this.options.onlyVisible?n.filter(":visible"):n).length&&(V.each(["first","last"],function(t,e){var i,s=n[e]().data("ui-controlgroup-data");s&&o["_"+s.widgetName+"Options"]?((i=o["_"+s.widgetName+"Options"](1===n.length?"only":e)).classes=o._resolveClassesValues(i.classes,s),s.element[s.widgetName](i)):o._updateCornerClass(n[e](),e)}),this._callChildMethod("refresh"))}});V.widget("ui.checkboxradio",[V.ui.formResetMixin,{version:"1.13.2",options:{disabled:null,label:null,icon:!0,classes:{"ui-checkboxradio-label":"ui-corner-all","ui-checkboxradio-icon":"ui-corner-all"}},_getCreateOptions:function(){var t,e=this._super()||{};return this._readType(),t=this.element.labels(),this.label=V(t[t.length-1]),this.label.length||V.error("No label found for checkboxradio widget"),this.originalLabel="",(t=this.label.contents().not(this.element[0])).length&&(this.originalLabel+=t.clone().wrapAll("<div></div>").parent().html()),this.originalLabel&&(e.label=this.originalLabel),null!=(t=this.element[0].disabled)&&(e.disabled=t),e},_create:function(){var t=this.element[0].checked;this._bindFormResetHandler(),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled),this._setOption("disabled",this.options.disabled),this._addClass("ui-checkboxradio","ui-helper-hidden-accessible"),this._addClass(this.label,"ui-checkboxradio-label","ui-button ui-widget"),"radio"===this.type&&this._addClass(this.label,"ui-checkboxradio-radio-label"),this.options.label&&this.options.label!==this.originalLabel?this._updateLabel():this.originalLabel&&(this.options.label=this.originalLabel),this._enhance(),t&&this._addClass(this.label,"ui-checkboxradio-checked","ui-state-active"),this._on({change:"_toggleClasses",focus:function(){this._addClass(this.label,null,"ui-state-focus ui-visual-focus")},blur:function(){this._removeClass(this.label,null,"ui-state-focus ui-visual-focus")}})},_readType:function(){var t=this.element[0].nodeName.toLowerCase();this.type=this.element[0].type,"input"===t&&/radio|checkbox/.test(this.type)||V.error("Can't create checkboxradio on element.nodeName="+t+" and element.type="+this.type)},_enhance:function(){this._updateIcon(this.element[0].checked)},widget:function(){return this.label},_getRadioGroup:function(){var t=this.element[0].name,e="input[name='"+V.escapeSelector(t)+"']";return t?(this.form.length?V(this.form[0].elements).filter(e):V(e).filter(function(){return 0===V(this)._form().length})).not(this.element):V([])},_toggleClasses:function(){var t=this.element[0].checked;this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),this.options.icon&&"checkbox"===this.type&&this._toggleClass(this.icon,null,"ui-icon-check ui-state-checked",t)._toggleClass(this.icon,null,"ui-icon-blank",!t),"radio"===this.type&&this._getRadioGroup().each(function(){var t=V(this).checkboxradio("instance");t&&t._removeClass(t.label,"ui-checkboxradio-checked","ui-state-active")})},_destroy:function(){this._unbindFormResetHandler(),this.icon&&(this.icon.remove(),this.iconSpace.remove())},_setOption:function(t,e){if("label"!==t||e){if(this._super(t,e),"disabled"===t)return this._toggleClass(this.label,null,"ui-state-disabled",e),void(this.element[0].disabled=e);this.refresh()}},_updateIcon:function(t){var e="ui-icon ui-icon-background ";this.options.icon?(this.icon||(this.icon=V("<span>"),this.iconSpace=V("<span> </span>"),this._addClass(this.iconSpace,"ui-checkboxradio-icon-space")),"checkbox"===this.type?(e+=t?"ui-icon-check ui-state-checked":"ui-icon-blank",this._removeClass(this.icon,null,t?"ui-icon-blank":"ui-icon-check")):e+="ui-icon-blank",this._addClass(this.icon,"ui-checkboxradio-icon",e),t||this._removeClass(this.icon,null,"ui-icon-check ui-state-checked"),this.icon.prependTo(this.label).after(this.iconSpace)):void 0!==this.icon&&(this.icon.remove(),this.iconSpace.remove(),delete this.icon)},_updateLabel:function(){var t=this.label.contents().not(this.element[0]);this.icon&&(t=t.not(this.icon[0])),(t=this.iconSpace?t.not(this.iconSpace[0]):t).remove(),this.label.append(this.options.label)},refresh:function(){var t=this.element[0].checked,e=this.element[0].disabled;this._updateIcon(t),this._toggleClass(this.label,"ui-checkboxradio-checked","ui-state-active",t),null!==this.options.label&&this._updateLabel(),e!==this.options.disabled&&this._setOptions({disabled:e})}}]);var et;V.ui.checkboxradio;V.widget("ui.button",{version:"1.13.2",defaultElement:"<button>",options:{classes:{"ui-button":"ui-corner-all"},disabled:null,icon:null,iconPosition:"beginning",label:null,showLabel:!0},_getCreateOptions:function(){var t,e=this._super()||{};return this.isInput=this.element.is("input"),null!=(t=this.element[0].disabled)&&(e.disabled=t),this.originalLabel=this.isInput?this.element.val():this.element.html(),this.originalLabel&&(e.label=this.originalLabel),e},_create:function(){!this.option.showLabel&!this.options.icon&&(this.options.showLabel=!0),null==this.options.disabled&&(this.options.disabled=this.element[0].disabled||!1),this.hasTitle=!!this.element.attr("title"),this.options.label&&this.options.label!==this.originalLabel&&(this.isInput?this.element.val(this.options.label):this.element.html(this.options.label)),this._addClass("ui-button","ui-widget"),this._setOption("disabled",this.options.disabled),this._enhance(),this.element.is("a")&&this._on({keyup:function(t){t.keyCode===V.ui.keyCode.SPACE&&(t.preventDefault(),this.element[0].click?this.element[0].click():this.element.trigger("click"))}})},_enhance:function(){this.element.is("button")||this.element.attr("role","button"),this.options.icon&&(this._updateIcon("icon",this.options.icon),this._updateTooltip())},_updateTooltip:function(){this.title=this.element.attr("title"),this.options.showLabel||this.title||this.element.attr("title",this.options.label)},_updateIcon:function(t,e){var i="iconPosition"!==t,s=i?this.options.iconPosition:e,t="top"===s||"bottom"===s;this.icon?i&&this._removeClass(this.icon,null,this.options.icon):(this.icon=V("<span>"),this._addClass(this.icon,"ui-button-icon","ui-icon"),this.options.showLabel||this._addClass("ui-button-icon-only")),i&&this._addClass(this.icon,null,e),this._attachIcon(s),t?(this._addClass(this.icon,null,"ui-widget-icon-block"),this.iconSpace&&this.iconSpace.remove()):(this.iconSpace||(this.iconSpace=V("<span> </span>"),this._addClass(this.iconSpace,"ui-button-icon-space")),this._removeClass(this.icon,null,"ui-wiget-icon-block"),this._attachIconSpace(s))},_destroy:function(){this.element.removeAttr("role"),this.icon&&this.icon.remove(),this.iconSpace&&this.iconSpace.remove(),this.hasTitle||this.element.removeAttr("title")},_attachIconSpace:function(t){this.icon[/^(?:end|bottom)/.test(t)?"before":"after"](this.iconSpace)},_attachIcon:function(t){this.element[/^(?:end|bottom)/.test(t)?"append":"prepend"](this.icon)},_setOptions:function(t){var e=(void 0===t.showLabel?this.options:t).showLabel,i=(void 0===t.icon?this.options:t).icon;e||i||(t.showLabel=!0),this._super(t)},_setOption:function(t,e){"icon"===t&&(e?this._updateIcon(t,e):this.icon&&(this.icon.remove(),this.iconSpace&&this.iconSpace.remove())),"iconPosition"===t&&this._updateIcon(t,e),"showLabel"===t&&(this._toggleClass("ui-button-icon-only",null,!e),this._updateTooltip()),"label"===t&&(this.isInput?this.element.val(e):(this.element.html(e),this.icon&&(this._attachIcon(this.options.iconPosition),this._attachIconSpace(this.options.iconPosition)))),this._super(t,e),"disabled"===t&&(this._toggleClass(null,"ui-state-disabled",e),(this.element[0].disabled=e)&&this.element.trigger("blur"))},refresh:function(){var t=this.element.is("input, button")?this.element[0].disabled:this.element.hasClass("ui-button-disabled");t!==this.options.disabled&&this._setOptions({disabled:t}),this._updateTooltip()}}),!1!==V.uiBackCompat&&(V.widget("ui.button",V.ui.button,{options:{text:!0,icons:{primary:null,secondary:null}},_create:function(){this.options.showLabel&&!this.options.text&&(this.options.showLabel=this.options.text),!this.options.showLabel&&this.options.text&&(this.options.text=this.options.showLabel),this.options.icon||!this.options.icons.primary&&!this.options.icons.secondary?this.options.icon&&(this.options.icons.primary=this.options.icon):this.options.icons.primary?this.options.icon=this.options.icons.primary:(this.options.icon=this.options.icons.secondary,this.options.iconPosition="end"),this._super()},_setOption:function(t,e){"text"!==t?("showLabel"===t&&(this.options.text=e),"icon"===t&&(this.options.icons.primary=e),"icons"===t&&(e.primary?(this._super("icon",e.primary),this._super("iconPosition","beginning")):e.secondary&&(this._super("icon",e.secondary),this._super("iconPosition","end"))),this._superApply(arguments)):this._super("showLabel",e)}}),V.fn.button=(et=V.fn.button,function(i){var t="string"==typeof i,s=Array.prototype.slice.call(arguments,1),n=this;return t?this.length||"instance"!==i?this.each(function(){var t=V(this).attr("type"),e=V.data(this,"ui-"+("checkbox"!==t&&"radio"!==t?"button":"checkboxradio"));return"instance"===i?(n=e,!1):e?"function"!=typeof e[i]||"_"===i.charAt(0)?V.error("no such method '"+i+"' for button widget instance"):(t=e[i].apply(e,s))!==e&&void 0!==t?(n=t&&t.jquery?n.pushStack(t.get()):t,!1):void 0:V.error("cannot call methods on button prior to initialization; attempted to call method '"+i+"'")}):n=void 0:(s.length&&(i=V.widget.extend.apply(null,[i].concat(s))),this.each(function(){var t=V(this).attr("type"),e="checkbox"!==t&&"radio"!==t?"button":"checkboxradio",t=V.data(this,"ui-"+e);t?(t.option(i||{}),t._init&&t._init()):"button"!=e?V(this).checkboxradio(V.extend({icon:!1},i)):et.call(V(this),i)})),n}),V.fn.buttonset=function(){return V.ui.controlgroup||V.error("Controlgroup widget missing"),"option"===arguments[0]&&"items"===arguments[1]&&arguments[2]?this.controlgroup.apply(this,[arguments[0],"items.button",arguments[2]]):"option"===arguments[0]&&"items"===arguments[1]?this.controlgroup.apply(this,[arguments[0],"items.button"]):("object"==typeof arguments[0]&&arguments[0].items&&(arguments[0].items={button:arguments[0].items}),this.controlgroup.apply(this,arguments))});var it;V.ui.button;function st(){this._curInst=null,this._keyEvent=!1,this._disabledInputs=[],this._datepickerShowing=!1,this._inDialog=!1,this._mainDivId="ui-datepicker-div",this._inlineClass="ui-datepicker-inline",this._appendClass="ui-datepicker-append",this._triggerClass="ui-datepicker-trigger",this._dialogClass="ui-datepicker-dialog",this._disableClass="ui-datepicker-disabled",this._unselectableClass="ui-datepicker-unselectable",this._currentClass="ui-datepicker-current-day",this._dayOverClass="ui-datepicker-days-cell-over",this.regional=[],this.regional[""]={closeText:"Done",prevText:"Prev",nextText:"Next",currentText:"Today",monthNames:["January","February","March","April","May","June","July","August","September","October","November","December"],monthNamesShort:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],dayNames:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],dayNamesShort:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],dayNamesMin:["Su","Mo","Tu","We","Th","Fr","Sa"],weekHeader:"Wk",dateFormat:"mm/dd/yy",firstDay:0,isRTL:!1,showMonthAfterYear:!1,yearSuffix:"",selectMonthLabel:"Select month",selectYearLabel:"Select year"},this._defaults={showOn:"focus",showAnim:"fadeIn",showOptions:{},defaultDate:null,appendText:"",buttonText:"...",buttonImage:"",buttonImageOnly:!1,hideIfNoPrevNext:!1,navigationAsDateFormat:!1,gotoCurrent:!1,changeMonth:!1,changeYear:!1,yearRange:"c-10:c+10",showOtherMonths:!1,selectOtherMonths:!1,showWeek:!1,calculateWeek:this.iso8601Week,shortYearCutoff:"+10",minDate:null,maxDate:null,duration:"fast",beforeShowDay:null,beforeShow:null,onSelect:null,onChangeMonthYear:null,onClose:null,onUpdateDatepicker:null,numberOfMonths:1,showCurrentAtPos:0,stepMonths:1,stepBigMonths:12,altField:"",altFormat:"",constrainInput:!0,showButtonPanel:!1,autoSize:!1,disabled:!1},V.extend(this._defaults,this.regional[""]),this.regional.en=V.extend(!0,{},this.regional[""]),this.regional["en-US"]=V.extend(!0,{},this.regional.en),this.dpDiv=nt(V("<div id='"+this._mainDivId+"' class='ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>"))}function nt(t){var e="button, .ui-datepicker-prev, .ui-datepicker-next, .ui-datepicker-calendar td a";return t.on("mouseout",e,function(){V(this).removeClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&V(this).removeClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&V(this).removeClass("ui-datepicker-next-hover")}).on("mouseover",e,ot)}function ot(){V.datepicker._isDisabledDatepicker((it.inline?it.dpDiv.parent():it.input)[0])||(V(this).parents(".ui-datepicker-calendar").find("a").removeClass("ui-state-hover"),V(this).addClass("ui-state-hover"),-1!==this.className.indexOf("ui-datepicker-prev")&&V(this).addClass("ui-datepicker-prev-hover"),-1!==this.className.indexOf("ui-datepicker-next")&&V(this).addClass("ui-datepicker-next-hover"))}function at(t,e){for(var i in V.extend(t,e),e)null==e[i]&&(t[i]=e[i]);return t}V.extend(V.ui,{datepicker:{version:"1.13.2"}}),V.extend(st.prototype,{markerClassName:"hasDatepicker",maxRows:4,_widgetDatepicker:function(){return this.dpDiv},setDefaults:function(t){return at(this._defaults,t||{}),this},_attachDatepicker:function(t,e){var i,s=t.nodeName.toLowerCase(),n="div"===s||"span"===s;t.id||(this.uuid+=1,t.id="dp"+this.uuid),(i=this._newInst(V(t),n)).settings=V.extend({},e||{}),"input"===s?this._connectDatepicker(t,i):n&&this._inlineDatepicker(t,i)},_newInst:function(t,e){return{id:t[0].id.replace(/([^A-Za-z0-9_\-])/g,"\\\\$1"),input:t,selectedDay:0,selectedMonth:0,selectedYear:0,drawMonth:0,drawYear:0,inline:e,dpDiv:e?nt(V("<div class='"+this._inlineClass+" ui-datepicker ui-widget ui-widget-content ui-helper-clearfix ui-corner-all'></div>")):this.dpDiv}},_connectDatepicker:function(t,e){var i=V(t);e.append=V([]),e.trigger=V([]),i.hasClass(this.markerClassName)||(this._attachments(i,e),i.addClass(this.markerClassName).on("keydown",this._doKeyDown).on("keypress",this._doKeyPress).on("keyup",this._doKeyUp),this._autoSize(e),V.data(t,"datepicker",e),e.settings.disabled&&this._disableDatepicker(t))},_attachments:function(t,e){var i,s=this._get(e,"appendText"),n=this._get(e,"isRTL");e.append&&e.append.remove(),s&&(e.append=V("<span>").addClass(this._appendClass).text(s),t[n?"before":"after"](e.append)),t.off("focus",this._showDatepicker),e.trigger&&e.trigger.remove(),"focus"!==(i=this._get(e,"showOn"))&&"both"!==i||t.on("focus",this._showDatepicker),"button"!==i&&"both"!==i||(s=this._get(e,"buttonText"),i=this._get(e,"buttonImage"),this._get(e,"buttonImageOnly")?e.trigger=V("<img>").addClass(this._triggerClass).attr({src:i,alt:s,title:s}):(e.trigger=V("<button type='button'>").addClass(this._triggerClass),i?e.trigger.html(V("<img>").attr({src:i,alt:s,title:s})):e.trigger.text(s)),t[n?"before":"after"](e.trigger),e.trigger.on("click",function(){return V.datepicker._datepickerShowing&&V.datepicker._lastInput===t[0]?V.datepicker._hideDatepicker():(V.datepicker._datepickerShowing&&V.datepicker._lastInput!==t[0]&&V.datepicker._hideDatepicker(),V.datepicker._showDatepicker(t[0])),!1}))},_autoSize:function(t){var e,i,s,n,o,a;this._get(t,"autoSize")&&!t.inline&&(o=new Date(2009,11,20),(a=this._get(t,"dateFormat")).match(/[DM]/)&&(e=function(t){for(n=s=i=0;n<t.length;n++)t[n].length>i&&(i=t[n].length,s=n);return s},o.setMonth(e(this._get(t,a.match(/MM/)?"monthNames":"monthNamesShort"))),o.setDate(e(this._get(t,a.match(/DD/)?"dayNames":"dayNamesShort"))+20-o.getDay())),t.input.attr("size",this._formatDate(t,o).length))},_inlineDatepicker:function(t,e){var i=V(t);i.hasClass(this.markerClassName)||(i.addClass(this.markerClassName).append(e.dpDiv),V.data(t,"datepicker",e),this._setDate(e,this._getDefaultDate(e),!0),this._updateDatepicker(e),this._updateAlternate(e),e.settings.disabled&&this._disableDatepicker(t),e.dpDiv.css("display","block"))},_dialogDatepicker:function(t,e,i,s,n){var o,a=this._dialogInst;return a||(this.uuid+=1,o="dp"+this.uuid,this._dialogInput=V("<input type='text' id='"+o+"' style='position: absolute; top: -100px; width: 0px;'/>"),this._dialogInput.on("keydown",this._doKeyDown),V("body").append(this._dialogInput),(a=this._dialogInst=this._newInst(this._dialogInput,!1)).settings={},V.data(this._dialogInput[0],"datepicker",a)),at(a.settings,s||{}),e=e&&e.constructor===Date?this._formatDate(a,e):e,this._dialogInput.val(e),this._pos=n?n.length?n:[n.pageX,n.pageY]:null,this._pos||(o=document.documentElement.clientWidth,s=document.documentElement.clientHeight,e=document.documentElement.scrollLeft||document.body.scrollLeft,n=document.documentElement.scrollTop||document.body.scrollTop,this._pos=[o/2-100+e,s/2-150+n]),this._dialogInput.css("left",this._pos[0]+20+"px").css("top",this._pos[1]+"px"),a.settings.onSelect=i,this._inDialog=!0,this.dpDiv.addClass(this._dialogClass),this._showDatepicker(this._dialogInput[0]),V.blockUI&&V.blockUI(this.dpDiv),V.data(this._dialogInput[0],"datepicker",a),this},_destroyDatepicker:function(t){var e,i=V(t),s=V.data(t,"datepicker");i.hasClass(this.markerClassName)&&(e=t.nodeName.toLowerCase(),V.removeData(t,"datepicker"),"input"===e?(s.append.remove(),s.trigger.remove(),i.removeClass(this.markerClassName).off("focus",this._showDatepicker).off("keydown",this._doKeyDown).off("keypress",this._doKeyPress).off("keyup",this._doKeyUp)):"div"!==e&&"span"!==e||i.removeClass(this.markerClassName).empty(),it===s&&(it=null,this._curInst=null))},_enableDatepicker:function(e){var t,i=V(e),s=V.data(e,"datepicker");i.hasClass(this.markerClassName)&&("input"===(t=e.nodeName.toLowerCase())?(e.disabled=!1,s.trigger.filter("button").each(function(){this.disabled=!1}).end().filter("img").css({opacity:"1.0",cursor:""})):"div"!==t&&"span"!==t||((i=i.children("."+this._inlineClass)).children().removeClass("ui-state-disabled"),i.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!1)),this._disabledInputs=V.map(this._disabledInputs,function(t){return t===e?null:t}))},_disableDatepicker:function(e){var t,i=V(e),s=V.data(e,"datepicker");i.hasClass(this.markerClassName)&&("input"===(t=e.nodeName.toLowerCase())?(e.disabled=!0,s.trigger.filter("button").each(function(){this.disabled=!0}).end().filter("img").css({opacity:"0.5",cursor:"default"})):"div"!==t&&"span"!==t||((i=i.children("."+this._inlineClass)).children().addClass("ui-state-disabled"),i.find("select.ui-datepicker-month, select.ui-datepicker-year").prop("disabled",!0)),this._disabledInputs=V.map(this._disabledInputs,function(t){return t===e?null:t}),this._disabledInputs[this._disabledInputs.length]=e)},_isDisabledDatepicker:function(t){if(!t)return!1;for(var e=0;e<this._disabledInputs.length;e++)if(this._disabledInputs[e]===t)return!0;return!1},_getInst:function(t){try{return V.data(t,"datepicker")}catch(t){throw"Missing instance data for this datepicker"}},_optionDatepicker:function(t,e,i){var s,n,o=this._getInst(t);if(2===arguments.length&&"string"==typeof e)return"defaults"===e?V.extend({},V.datepicker._defaults):o?"all"===e?V.extend({},o.settings):this._get(o,e):null;s=e||{},"string"==typeof e&&((s={})[e]=i),o&&(this._curInst===o&&this._hideDatepicker(),n=this._getDateDatepicker(t,!0),e=this._getMinMaxDate(o,"min"),i=this._getMinMaxDate(o,"max"),at(o.settings,s),null!==e&&void 0!==s.dateFormat&&void 0===s.minDate&&(o.settings.minDate=this._formatDate(o,e)),null!==i&&void 0!==s.dateFormat&&void 0===s.maxDate&&(o.settings.maxDate=this._formatDate(o,i)),"disabled"in s&&(s.disabled?this._disableDatepicker(t):this._enableDatepicker(t)),this._attachments(V(t),o),this._autoSize(o),this._setDate(o,n),this._updateAlternate(o),this._updateDatepicker(o))},_changeDatepicker:function(t,e,i){this._optionDatepicker(t,e,i)},_refreshDatepicker:function(t){t=this._getInst(t);t&&this._updateDatepicker(t)},_setDateDatepicker:function(t,e){t=this._getInst(t);t&&(this._setDate(t,e),this._updateDatepicker(t),this._updateAlternate(t))},_getDateDatepicker:function(t,e){t=this._getInst(t);return t&&!t.inline&&this._setDateFromField(t,e),t?this._getDate(t):null},_doKeyDown:function(t){var e,i,s=V.datepicker._getInst(t.target),n=!0,o=s.dpDiv.is(".ui-datepicker-rtl");if(s._keyEvent=!0,V.datepicker._datepickerShowing)switch(t.keyCode){case 9:V.datepicker._hideDatepicker(),n=!1;break;case 13:return(i=V("td."+V.datepicker._dayOverClass+":not(."+V.datepicker._currentClass+")",s.dpDiv))[0]&&V.datepicker._selectDay(t.target,s.selectedMonth,s.selectedYear,i[0]),(e=V.datepicker._get(s,"onSelect"))?(i=V.datepicker._formatDate(s),e.apply(s.input?s.input[0]:null,[i,s])):V.datepicker._hideDatepicker(),!1;case 27:V.datepicker._hideDatepicker();break;case 33:V.datepicker._adjustDate(t.target,t.ctrlKey?-V.datepicker._get(s,"stepBigMonths"):-V.datepicker._get(s,"stepMonths"),"M");break;case 34:V.datepicker._adjustDate(t.target,t.ctrlKey?+V.datepicker._get(s,"stepBigMonths"):+V.datepicker._get(s,"stepMonths"),"M");break;case 35:(t.ctrlKey||t.metaKey)&&V.datepicker._clearDate(t.target),n=t.ctrlKey||t.metaKey;break;case 36:(t.ctrlKey||t.metaKey)&&V.datepicker._gotoToday(t.target),n=t.ctrlKey||t.metaKey;break;case 37:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,o?1:-1,"D"),n=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&V.datepicker._adjustDate(t.target,t.ctrlKey?-V.datepicker._get(s,"stepBigMonths"):-V.datepicker._get(s,"stepMonths"),"M");break;case 38:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,-7,"D"),n=t.ctrlKey||t.metaKey;break;case 39:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,o?-1:1,"D"),n=t.ctrlKey||t.metaKey,t.originalEvent.altKey&&V.datepicker._adjustDate(t.target,t.ctrlKey?+V.datepicker._get(s,"stepBigMonths"):+V.datepicker._get(s,"stepMonths"),"M");break;case 40:(t.ctrlKey||t.metaKey)&&V.datepicker._adjustDate(t.target,7,"D"),n=t.ctrlKey||t.metaKey;break;default:n=!1}else 36===t.keyCode&&t.ctrlKey?V.datepicker._showDatepicker(this):n=!1;n&&(t.preventDefault(),t.stopPropagation())},_doKeyPress:function(t){var e,i=V.datepicker._getInst(t.target);if(V.datepicker._get(i,"constrainInput"))return e=V.datepicker._possibleChars(V.datepicker._get(i,"dateFormat")),i=String.fromCharCode(null==t.charCode?t.keyCode:t.charCode),t.ctrlKey||t.metaKey||i<" "||!e||-1<e.indexOf(i)},_doKeyUp:function(t){t=V.datepicker._getInst(t.target);if(t.input.val()!==t.lastVal)try{V.datepicker.parseDate(V.datepicker._get(t,"dateFormat"),t.input?t.input.val():null,V.datepicker._getFormatConfig(t))&&(V.datepicker._setDateFromField(t),V.datepicker._updateAlternate(t),V.datepicker._updateDatepicker(t))}catch(t){}return!0},_showDatepicker:function(t){var e,i,s,n;"input"!==(t=t.target||t).nodeName.toLowerCase()&&(t=V("input",t.parentNode)[0]),V.datepicker._isDisabledDatepicker(t)||V.datepicker._lastInput===t||(n=V.datepicker._getInst(t),V.datepicker._curInst&&V.datepicker._curInst!==n&&(V.datepicker._curInst.dpDiv.stop(!0,!0),n&&V.datepicker._datepickerShowing&&V.datepicker._hideDatepicker(V.datepicker._curInst.input[0])),!1!==(i=(s=V.datepicker._get(n,"beforeShow"))?s.apply(t,[t,n]):{})&&(at(n.settings,i),n.lastVal=null,V.datepicker._lastInput=t,V.datepicker._setDateFromField(n),V.datepicker._inDialog&&(t.value=""),V.datepicker._pos||(V.datepicker._pos=V.datepicker._findPos(t),V.datepicker._pos[1]+=t.offsetHeight),e=!1,V(t).parents().each(function(){return!(e|="fixed"===V(this).css("position"))}),s={left:V.datepicker._pos[0],top:V.datepicker._pos[1]},V.datepicker._pos=null,n.dpDiv.empty(),n.dpDiv.css({position:"absolute",display:"block",top:"-1000px"}),V.datepicker._updateDatepicker(n),s=V.datepicker._checkOffset(n,s,e),n.dpDiv.css({position:V.datepicker._inDialog&&V.blockUI?"static":e?"fixed":"absolute",display:"none",left:s.left+"px",top:s.top+"px"}),n.inline||(i=V.datepicker._get(n,"showAnim"),s=V.datepicker._get(n,"duration"),n.dpDiv.css("z-index",function(t){for(var e,i;t.length&&t[0]!==document;){if(("absolute"===(e=t.css("position"))||"relative"===e||"fixed"===e)&&(i=parseInt(t.css("zIndex"),10),!isNaN(i)&&0!==i))return i;t=t.parent()}return 0}(V(t))+1),V.datepicker._datepickerShowing=!0,V.effects&&V.effects.effect[i]?n.dpDiv.show(i,V.datepicker._get(n,"showOptions"),s):n.dpDiv[i||"show"](i?s:null),V.datepicker._shouldFocusInput(n)&&n.input.trigger("focus"),V.datepicker._curInst=n)))},_updateDatepicker:function(t){this.maxRows=4,(it=t).dpDiv.empty().append(this._generateHTML(t)),this._attachHandlers(t);var e,i=this._getNumberOfMonths(t),s=i[1],n=t.dpDiv.find("."+this._dayOverClass+" a"),o=V.datepicker._get(t,"onUpdateDatepicker");0<n.length&&ot.apply(n.get(0)),t.dpDiv.removeClass("ui-datepicker-multi-2 ui-datepicker-multi-3 ui-datepicker-multi-4").width(""),1<s&&t.dpDiv.addClass("ui-datepicker-multi-"+s).css("width",17*s+"em"),t.dpDiv[(1!==i[0]||1!==i[1]?"add":"remove")+"Class"]("ui-datepicker-multi"),t.dpDiv[(this._get(t,"isRTL")?"add":"remove")+"Class"]("ui-datepicker-rtl"),t===V.datepicker._curInst&&V.datepicker._datepickerShowing&&V.datepicker._shouldFocusInput(t)&&t.input.trigger("focus"),t.yearshtml&&(e=t.yearshtml,setTimeout(function(){e===t.yearshtml&&t.yearshtml&&t.dpDiv.find("select.ui-datepicker-year").first().replaceWith(t.yearshtml),e=t.yearshtml=null},0)),o&&o.apply(t.input?t.input[0]:null,[t])},_shouldFocusInput:function(t){return t.input&&t.input.is(":visible")&&!t.input.is(":disabled")&&!t.input.is(":focus")},_checkOffset:function(t,e,i){var s=t.dpDiv.outerWidth(),n=t.dpDiv.outerHeight(),o=t.input?t.input.outerWidth():0,a=t.input?t.input.outerHeight():0,r=document.documentElement.clientWidth+(i?0:V(document).scrollLeft()),l=document.documentElement.clientHeight+(i?0:V(document).scrollTop());return e.left-=this._get(t,"isRTL")?s-o:0,e.left-=i&&e.left===t.input.offset().left?V(document).scrollLeft():0,e.top-=i&&e.top===t.input.offset().top+a?V(document).scrollTop():0,e.left-=Math.min(e.left,e.left+s>r&&s<r?Math.abs(e.left+s-r):0),e.top-=Math.min(e.top,e.top+n>l&&n<l?Math.abs(n+a):0),e},_findPos:function(t){for(var e=this._getInst(t),i=this._get(e,"isRTL");t&&("hidden"===t.type||1!==t.nodeType||V.expr.pseudos.hidden(t));)t=t[i?"previousSibling":"nextSibling"];return[(e=V(t).offset()).left,e.top]},_hideDatepicker:function(t){var e,i,s=this._curInst;!s||t&&s!==V.data(t,"datepicker")||this._datepickerShowing&&(e=this._get(s,"showAnim"),i=this._get(s,"duration"),t=function(){V.datepicker._tidyDialog(s)},V.effects&&(V.effects.effect[e]||V.effects[e])?s.dpDiv.hide(e,V.datepicker._get(s,"showOptions"),i,t):s.dpDiv["slideDown"===e?"slideUp":"fadeIn"===e?"fadeOut":"hide"](e?i:null,t),e||t(),this._datepickerShowing=!1,(t=this._get(s,"onClose"))&&t.apply(s.input?s.input[0]:null,[s.input?s.input.val():"",s]),this._lastInput=null,this._inDialog&&(this._dialogInput.css({position:"absolute",left:"0",top:"-100px"}),V.blockUI&&(V.unblockUI(),V("body").append(this.dpDiv))),this._inDialog=!1)},_tidyDialog:function(t){t.dpDiv.removeClass(this._dialogClass).off(".ui-datepicker-calendar")},_checkExternalClick:function(t){var e;V.datepicker._curInst&&(e=V(t.target),t=V.datepicker._getInst(e[0]),(e[0].id===V.datepicker._mainDivId||0!==e.parents("#"+V.datepicker._mainDivId).length||e.hasClass(V.datepicker.markerClassName)||e.closest("."+V.datepicker._triggerClass).length||!V.datepicker._datepickerShowing||V.datepicker._inDialog&&V.blockUI)&&(!e.hasClass(V.datepicker.markerClassName)||V.datepicker._curInst===t)||V.datepicker._hideDatepicker())},_adjustDate:function(t,e,i){var s=V(t),t=this._getInst(s[0]);this._isDisabledDatepicker(s[0])||(this._adjustInstDate(t,e,i),this._updateDatepicker(t))},_gotoToday:function(t){var e=V(t),i=this._getInst(e[0]);this._get(i,"gotoCurrent")&&i.currentDay?(i.selectedDay=i.currentDay,i.drawMonth=i.selectedMonth=i.currentMonth,i.drawYear=i.selectedYear=i.currentYear):(t=new Date,i.selectedDay=t.getDate(),i.drawMonth=i.selectedMonth=t.getMonth(),i.drawYear=i.selectedYear=t.getFullYear()),this._notifyChange(i),this._adjustDate(e)},_selectMonthYear:function(t,e,i){var s=V(t),t=this._getInst(s[0]);t["selected"+("M"===i?"Month":"Year")]=t["draw"+("M"===i?"Month":"Year")]=parseInt(e.options[e.selectedIndex].value,10),this._notifyChange(t),this._adjustDate(s)},_selectDay:function(t,e,i,s){var n=V(t);V(s).hasClass(this._unselectableClass)||this._isDisabledDatepicker(n[0])||((n=this._getInst(n[0])).selectedDay=n.currentDay=parseInt(V("a",s).attr("data-date")),n.selectedMonth=n.currentMonth=e,n.selectedYear=n.currentYear=i,this._selectDate(t,this._formatDate(n,n.currentDay,n.currentMonth,n.currentYear)))},_clearDate:function(t){t=V(t);this._selectDate(t,"")},_selectDate:function(t,e){var i=V(t),t=this._getInst(i[0]);e=null!=e?e:this._formatDate(t),t.input&&t.input.val(e),this._updateAlternate(t),(i=this._get(t,"onSelect"))?i.apply(t.input?t.input[0]:null,[e,t]):t.input&&t.input.trigger("change"),t.inline?this._updateDatepicker(t):(this._hideDatepicker(),this._lastInput=t.input[0],"object"!=typeof t.input[0]&&t.input.trigger("focus"),this._lastInput=null)},_updateAlternate:function(t){var e,i,s=this._get(t,"altField");s&&(e=this._get(t,"altFormat")||this._get(t,"dateFormat"),i=this._getDate(t),t=this.formatDate(e,i,this._getFormatConfig(t)),V(document).find(s).val(t))},noWeekends:function(t){t=t.getDay();return[0<t&&t<6,""]},iso8601Week:function(t){var e=new Date(t.getTime());return e.setDate(e.getDate()+4-(e.getDay()||7)),t=e.getTime(),e.setMonth(0),e.setDate(1),Math.floor(Math.round((t-e)/864e5)/7)+1},parseDate:function(e,n,t){if(null==e||null==n)throw"Invalid arguments";if(""===(n="object"==typeof n?n.toString():n+""))return null;for(var i,s,o,a=0,r=(t?t.shortYearCutoff:null)||this._defaults.shortYearCutoff,r="string"!=typeof r?r:(new Date).getFullYear()%100+parseInt(r,10),l=(t?t.dayNamesShort:null)||this._defaults.dayNamesShort,h=(t?t.dayNames:null)||this._defaults.dayNames,c=(t?t.monthNamesShort:null)||this._defaults.monthNamesShort,u=(t?t.monthNames:null)||this._defaults.monthNames,d=-1,p=-1,f=-1,g=-1,m=!1,_=function(t){t=w+1<e.length&&e.charAt(w+1)===t;return t&&w++,t},v=function(t){var e=_(t),e="@"===t?14:"!"===t?20:"y"===t&&e?4:"o"===t?3:2,e=new RegExp("^\\d{"+("y"===t?e:1)+","+e+"}"),e=n.substring(a).match(e);if(!e)throw"Missing number at position "+a;return a+=e[0].length,parseInt(e[0],10)},b=function(t,e,i){var s=-1,e=V.map(_(t)?i:e,function(t,e){return[[e,t]]}).sort(function(t,e){return-(t[1].length-e[1].length)});if(V.each(e,function(t,e){var i=e[1];if(n.substr(a,i.length).toLowerCase()===i.toLowerCase())return s=e[0],a+=i.length,!1}),-1!==s)return s+1;throw"Unknown name at position "+a},y=function(){if(n.charAt(a)!==e.charAt(w))throw"Unexpected literal at position 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dd-M-y",RFC_1036:"D, d M y",RFC_1123:"D, d M yy",RFC_2822:"D, d M yy",RSS:"D, d M y",TICKS:"!",TIMESTAMP:"@",W3C:"yy-mm-dd",_ticksTo1970:24*(718685+Math.floor(492.5)-Math.floor(19.7)+Math.floor(4.925))*60*60*1e7,formatDate:function(e,t,i){if(!t)return"";function s(t,e,i){var s=""+e;if(c(t))for(;s.length<i;)s="0"+s;return s}function n(t,e,i,s){return(c(t)?s:i)[e]}var o,a=(i?i.dayNamesShort:null)||this._defaults.dayNamesShort,r=(i?i.dayNames:null)||this._defaults.dayNames,l=(i?i.monthNamesShort:null)||this._defaults.monthNamesShort,h=(i?i.monthNames:null)||this._defaults.monthNames,c=function(t){t=o+1<e.length&&e.charAt(o+1)===t;return t&&o++,t},u="",d=!1;if(t)for(o=0;o<e.length;o++)if(d)"'"!==e.charAt(o)||c("'")?u+=e.charAt(o):d=!1;else switch(e.charAt(o)){case"d":u+=s("d",t.getDate(),2);break;case"D":u+=n("D",t.getDay(),a,r);break;case"o":u+=s("o",Math.round((new Date(t.getFullYear(),t.getMonth(),t.getDate()).getTime()-new Date(t.getFullYear(),0,0).getTime())/864e5),3);break;case"m":u+=s("m",t.getMonth()+1,2);break;case"M":u+=n("M",t.getMonth(),l,h);break;case"y":u+=c("y")?t.getFullYear():(t.getFullYear()%100<10?"0":"")+t.getFullYear()%100;break;case"@":u+=t.getTime();break;case"!":u+=1e4*t.getTime()+this._ticksTo1970;break;case"'":c("'")?u+="'":d=!0;break;default:u+=e.charAt(o)}return u},_possibleChars:function(e){for(var t="",i=!1,s=function(t){t=n+1<e.length&&e.charAt(n+1)===t;return t&&n++,t},n=0;n<e.length;n++)if(i)"'"!==e.charAt(n)||s("'")?t+=e.charAt(n):i=!1;else switch(e.charAt(n)){case"d":case"m":case"y":case"@":t+="0123456789";break;case"D":case"M":return null;case"'":s("'")?t+="'":i=!0;break;default:t+=e.charAt(n)}return t},_get:function(t,e){return(void 0!==t.settings[e]?t.settings:this._defaults)[e]},_setDateFromField:function(t,e){if(t.input.val()!==t.lastVal){var i=this._get(t,"dateFormat"),s=t.lastVal=t.input?t.input.val():null,n=this._getDefaultDate(t),o=n,a=this._getFormatConfig(t);try{o=this.parseDate(i,s,a)||n}catch(t){s=e?"":s}t.selectedDay=o.getDate(),t.drawMonth=t.selectedMonth=o.getMonth(),t.drawYear=t.selectedYear=o.getFullYear(),t.currentDay=s?o.getDate():0,t.currentMonth=s?o.getMonth():0,t.currentYear=s?o.getFullYear():0,this._adjustInstDate(t)}},_getDefaultDate:function(t){return this._restrictMinMax(t,this._determineDate(t,this._get(t,"defaultDate"),new Date))},_determineDate:function(r,t,e){var i,s,t=null==t||""===t?e:"string"==typeof t?function(t){try{return V.datepicker.parseDate(V.datepicker._get(r,"dateFormat"),t,V.datepicker._getFormatConfig(r))}catch(t){}for(var e=(t.toLowerCase().match(/^c/)?V.datepicker._getDate(r):null)||new Date,i=e.getFullYear(),s=e.getMonth(),n=e.getDate(),o=/([+\-]?[0-9]+)\s*(d|D|w|W|m|M|y|Y)?/g,a=o.exec(t);a;){switch(a[2]||"d"){case"d":case"D":n+=parseInt(a[1],10);break;case"w":case"W":n+=7*parseInt(a[1],10);break;case"m":case"M":s+=parseInt(a[1],10),n=Math.min(n,V.datepicker._getDaysInMonth(i,s));break;case"y":case"Y":i+=parseInt(a[1],10),n=Math.min(n,V.datepicker._getDaysInMonth(i,s))}a=o.exec(t)}return new Date(i,s,n)}(t):"number"==typeof t?isNaN(t)?e:(i=t,(s=new Date).setDate(s.getDate()+i),s):new Date(t.getTime());return(t=t&&"Invalid Date"===t.toString()?e:t)&&(t.setHours(0),t.setMinutes(0),t.setSeconds(0),t.setMilliseconds(0)),this._daylightSavingAdjust(t)},_daylightSavingAdjust:function(t){return t?(t.setHours(12<t.getHours()?t.getHours()+2:0),t):null},_setDate:function(t,e,i){var s=!e,n=t.selectedMonth,o=t.selectedYear,e=this._restrictMinMax(t,this._determineDate(t,e,new Date));t.selectedDay=t.currentDay=e.getDate(),t.drawMonth=t.selectedMonth=t.currentMonth=e.getMonth(),t.drawYear=t.selectedYear=t.currentYear=e.getFullYear(),n===t.selectedMonth&&o===t.selectedYear||i||this._notifyChange(t),this._adjustInstDate(t),t.input&&t.input.val(s?"":this._formatDate(t))},_getDate:function(t){return!t.currentYear||t.input&&""===t.input.val()?null:this._daylightSavingAdjust(new Date(t.currentYear,t.currentMonth,t.currentDay))},_attachHandlers:function(t){var e=this._get(t,"stepMonths"),i="#"+t.id.replace(/\\\\/g,"\\");t.dpDiv.find("[data-handler]").map(function(){var t={prev:function(){V.datepicker._adjustDate(i,-e,"M")},next:function(){V.datepicker._adjustDate(i,+e,"M")},hide:function(){V.datepicker._hideDatepicker()},today:function(){V.datepicker._gotoToday(i)},selectDay:function(){return V.datepicker._selectDay(i,+this.getAttribute("data-month"),+this.getAttribute("data-year"),this),!1},selectMonth:function(){return V.datepicker._selectMonthYear(i,this,"M"),!1},selectYear:function(){return V.datepicker._selectMonthYear(i,this,"Y"),!1}};V(this).on(this.getAttribute("data-event"),t[this.getAttribute("data-handler")])})},_generateHTML:function(t){var e,i,s,n,o,a,r,l,h,c,u,d,p,f,g,m,_,v,b,y,w,x,k,C,D,I,T,P,M,S,H,z,A=new Date,O=this._daylightSavingAdjust(new Date(A.getFullYear(),A.getMonth(),A.getDate())),N=this._get(t,"isRTL"),E=this._get(t,"showButtonPanel"),W=this._get(t,"hideIfNoPrevNext"),F=this._get(t,"navigationAsDateFormat"),L=this._getNumberOfMonths(t),R=this._get(t,"showCurrentAtPos"),A=this._get(t,"stepMonths"),Y=1!==L[0]||1!==L[1],B=this._daylightSavingAdjust(t.currentDay?new Date(t.currentYear,t.currentMonth,t.currentDay):new Date(9999,9,9)),j=this._getMinMaxDate(t,"min"),q=this._getMinMaxDate(t,"max"),K=t.drawMonth-R,U=t.drawYear;if(K<0&&(K+=12,U--),q)for(e=this._daylightSavingAdjust(new Date(q.getFullYear(),q.getMonth()-L[0]*L[1]+1,q.getDate())),e=j&&e<j?j:e;this._daylightSavingAdjust(new Date(U,K,1))>e;)--K<0&&(K=11,U--);for(t.drawMonth=K,t.drawYear=U,R=this._get(t,"prevText"),R=F?this.formatDate(R,this._daylightSavingAdjust(new Date(U,K-A,1)),this._getFormatConfig(t)):R,i=this._canAdjustMonth(t,-1,U,K)?V("<a>").attr({class:"ui-datepicker-prev ui-corner-all","data-handler":"prev","data-event":"click",title:R}).append(V("<span>").addClass("ui-icon ui-icon-circle-triangle-"+(N?"e":"w")).text(R))[0].outerHTML:W?"":V("<a>").attr({class:"ui-datepicker-prev ui-corner-all ui-state-disabled",title:R}).append(V("<span>").addClass("ui-icon ui-icon-circle-triangle-"+(N?"e":"w")).text(R))[0].outerHTML,R=this._get(t,"nextText"),R=F?this.formatDate(R,this._daylightSavingAdjust(new Date(U,K+A,1)),this._getFormatConfig(t)):R,s=this._canAdjustMonth(t,1,U,K)?V("<a>").attr({class:"ui-datepicker-next ui-corner-all","data-handler":"next","data-event":"click",title:R}).append(V("<span>").addClass("ui-icon ui-icon-circle-triangle-"+(N?"w":"e")).text(R))[0].outerHTML:W?"":V("<a>").attr({class:"ui-datepicker-next ui-corner-all ui-state-disabled",title:R}).append(V("<span>").attr("class","ui-icon ui-icon-circle-triangle-"+(N?"w":"e")).text(R))[0].outerHTML,A=this._get(t,"currentText"),W=this._get(t,"gotoCurrent")&&t.currentDay?B:O,A=F?this.formatDate(A,W,this._getFormatConfig(t)):A,R="",t.inline||(R=V("<button>").attr({type:"button",class:"ui-datepicker-close ui-state-default ui-priority-primary ui-corner-all","data-handler":"hide","data-event":"click"}).text(this._get(t,"closeText"))[0].outerHTML),F="",E&&(F=V("<div class='ui-datepicker-buttonpane ui-widget-content'>").append(N?R:"").append(this._isInRange(t,W)?V("<button>").attr({type:"button",class:"ui-datepicker-current ui-state-default ui-priority-secondary 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ui-datepicker-week-end":"")+(H?" ui-datepicker-other-month":"")+(T.getTime()===b.getTime()&&K===t.selectedMonth&&t._keyEvent||p.getTime()===T.getTime()&&p.getTime()===b.getTime()?" "+this._dayOverClass:"")+(z?" "+this._unselectableClass+" ui-state-disabled":"")+(H&&!u?"":" "+S[1]+(T.getTime()===B.getTime()?" 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t,e,i,s=this.options,n=this.document[0];this.relativeContainer=null,s.containment?"window"!==s.containment?"document"!==s.containment?s.containment.constructor!==Array?("parent"===s.containment&&(s.containment=this.helper[0].parentNode),(i=(e=V(s.containment))[0])&&(t=/(scroll|auto)/.test(e.css("overflow")),this.containment=[(parseInt(e.css("borderLeftWidth"),10)||0)+(parseInt(e.css("paddingLeft"),10)||0),(parseInt(e.css("borderTopWidth"),10)||0)+(parseInt(e.css("paddingTop"),10)||0),(t?Math.max(i.scrollWidth,i.offsetWidth):i.offsetWidth)-(parseInt(e.css("borderRightWidth"),10)||0)-(parseInt(e.css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left-this.margins.right,(t?Math.max(i.scrollHeight,i.offsetHeight):i.offsetHeight)-(parseInt(e.css("borderBottomWidth"),10)||0)-(parseInt(e.css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top-this.margins.bottom],this.relativeContainer=e)):this.containment=s.containment:this.containment=[0,0,V(n).width()-this.helperProportions.width-this.margins.left,(V(n).height()||n.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]:this.containment=[V(window).scrollLeft()-this.offset.relative.left-this.offset.parent.left,V(window).scrollTop()-this.offset.relative.top-this.offset.parent.top,V(window).scrollLeft()+V(window).width()-this.helperProportions.width-this.margins.left,V(window).scrollTop()+(V(window).height()||n.body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]:this.containment=null},_convertPositionTo:function(t,e){e=e||this.position;var i="absolute"===t?1:-1,t=this._isRootNode(this.scrollParent[0]);return{top:e.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.offset.scroll.top:t?0:this.offset.scroll.top)*i,left:e.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.offset.scroll.left:t?0:this.offset.scroll.left)*i}},_generatePosition:function(t,e){var i,s=this.options,n=this._isRootNode(this.scrollParent[0]),o=t.pageX,a=t.pageY;return n&&this.offset.scroll||(this.offset.scroll={top:this.scrollParent.scrollTop(),left:this.scrollParent.scrollLeft()}),e&&(this.containment&&(i=this.relativeContainer?(i=this.relativeContainer.offset(),[this.containment[0]+i.left,this.containment[1]+i.top,this.containment[2]+i.left,this.containment[3]+i.top]):this.containment,t.pageX-this.offset.click.left<i[0]&&(o=i[0]+this.offset.click.left),t.pageY-this.offset.click.top<i[1]&&(a=i[1]+this.offset.click.top),t.pageX-this.offset.click.left>i[2]&&(o=i[2]+this.offset.click.left),t.pageY-this.offset.click.top>i[3]&&(a=i[3]+this.offset.click.top)),s.grid&&(t=s.grid[1]?this.originalPageY+Math.round((a-this.originalPageY)/s.grid[1])*s.grid[1]:this.originalPageY,a=!i||t-this.offset.click.top>=i[1]||t-this.offset.click.top>i[3]?t:t-this.offset.click.top>=i[1]?t-s.grid[1]:t+s.grid[1],t=s.grid[0]?this.originalPageX+Math.round((o-this.originalPageX)/s.grid[0])*s.grid[0]:this.originalPageX,o=!i||t-this.offset.click.left>=i[0]||t-this.offset.click.left>i[2]?t:t-this.offset.click.left>=i[0]?t-s.grid[0]:t+s.grid[0]),"y"===s.axis&&(o=this.originalPageX),"x"===s.axis&&(a=this.originalPageY)),{top:a-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.offset.scroll.top:n?0:this.offset.scroll.top),left:o-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.offset.scroll.left:n?0:this.offset.scroll.left)}},_clear:function(){this._removeClass(this.helper,"ui-draggable-dragging"),this.helper[0]===this.element[0]||this.cancelHelperRemoval||this.helper.remove(),this.helper=null,this.cancelHelperRemoval=!1,this.destroyOnClear&&this.destroy()},_trigger:function(t,e,i){return i=i||this._uiHash(),V.ui.plugin.call(this,t,[e,i,this],!0),/^(drag|start|stop)/.test(t)&&(this.positionAbs=this._convertPositionTo("absolute"),i.offset=this.positionAbs),V.Widget.prototype._trigger.call(this,t,e,i)},plugins:{},_uiHash:function(){return{helper:this.helper,position:this.position,originalPosition:this.originalPosition,offset:this.positionAbs}}}),V.ui.plugin.add("draggable","connectToSortable",{start:function(e,t,i){var s=V.extend({},t,{item:i.element});i.sortables=[],V(i.options.connectToSortable).each(function(){var t=V(this).sortable("instance");t&&!t.options.disabled&&(i.sortables.push(t),t.refreshPositions(),t._trigger("activate",e,s))})},stop:function(e,t,i){var s=V.extend({},t,{item:i.element});i.cancelHelperRemoval=!1,V.each(i.sortables,function(){var t=this;t.isOver?(t.isOver=0,i.cancelHelperRemoval=!0,t.cancelHelperRemoval=!1,t._storedCSS={position:t.placeholder.css("position"),top:t.placeholder.css("top"),left:t.placeholder.css("left")},t._mouseStop(e),t.options.helper=t.options._helper):(t.cancelHelperRemoval=!0,t._trigger("deactivate",e,s))})},drag:function(i,s,n){V.each(n.sortables,function(){var t=!1,e=this;e.positionAbs=n.positionAbs,e.helperProportions=n.helperProportions,e.offset.click=n.offset.click,e._intersectsWith(e.containerCache)&&(t=!0,V.each(n.sortables,function(){return this.positionAbs=n.positionAbs,this.helperProportions=n.helperProportions,this.offset.click=n.offset.click,t=this!==e&&this._intersectsWith(this.containerCache)&&V.contains(e.element[0],this.element[0])?!1:t})),t?(e.isOver||(e.isOver=1,n._parent=s.helper.parent(),e.currentItem=s.helper.appendTo(e.element).data("ui-sortable-item",!0),e.options._helper=e.options.helper,e.options.helper=function(){return s.helper[0]},i.target=e.currentItem[0],e._mouseCapture(i,!0),e._mouseStart(i,!0,!0),e.offset.click.top=n.offset.click.top,e.offset.click.left=n.offset.click.left,e.offset.parent.left-=n.offset.parent.left-e.offset.parent.left,e.offset.parent.top-=n.offset.parent.top-e.offset.parent.top,n._trigger("toSortable",i),n.dropped=e.element,V.each(n.sortables,function(){this.refreshPositions()}),n.currentItem=n.element,e.fromOutside=n),e.currentItem&&(e._mouseDrag(i),s.position=e.position)):e.isOver&&(e.isOver=0,e.cancelHelperRemoval=!0,e.options._revert=e.options.revert,e.options.revert=!1,e._trigger("out",i,e._uiHash(e)),e._mouseStop(i,!0),e.options.revert=e.options._revert,e.options.helper=e.options._helper,e.placeholder&&e.placeholder.remove(),s.helper.appendTo(n._parent),n._refreshOffsets(i),s.position=n._generatePosition(i,!0),n._trigger("fromSortable",i),n.dropped=!1,V.each(n.sortables,function(){this.refreshPositions()}))})}}),V.ui.plugin.add("draggable","cursor",{start:function(t,e,i){var s=V("body"),i=i.options;s.css("cursor")&&(i._cursor=s.css("cursor")),s.css("cursor",i.cursor)},stop:function(t,e,i){i=i.options;i._cursor&&V("body").css("cursor",i._cursor)}}),V.ui.plugin.add("draggable","opacity",{start:function(t,e,i){e=V(e.helper),i=i.options;e.css("opacity")&&(i._opacity=e.css("opacity")),e.css("opacity",i.opacity)},stop:function(t,e,i){i=i.options;i._opacity&&V(e.helper).css("opacity",i._opacity)}}),V.ui.plugin.add("draggable","scroll",{start:function(t,e,i){i.scrollParentNotHidden||(i.scrollParentNotHidden=i.helper.scrollParent(!1)),i.scrollParentNotHidden[0]!==i.document[0]&&"HTML"!==i.scrollParentNotHidden[0].tagName&&(i.overflowOffset=i.scrollParentNotHidden.offset())},drag:function(t,e,i){var s=i.options,n=!1,o=i.scrollParentNotHidden[0],a=i.document[0];o!==a&&"HTML"!==o.tagName?(s.axis&&"x"===s.axis||(i.overflowOffset.top+o.offsetHeight-t.pageY<s.scrollSensitivity?o.scrollTop=n=o.scrollTop+s.scrollSpeed:t.pageY-i.overflowOffset.top<s.scrollSensitivity&&(o.scrollTop=n=o.scrollTop-s.scrollSpeed)),s.axis&&"y"===s.axis||(i.overflowOffset.left+o.offsetWidth-t.pageX<s.scrollSensitivity?o.scrollLeft=n=o.scrollLeft+s.scrollSpeed:t.pageX-i.overflowOffset.left<s.scrollSensitivity&&(o.scrollLeft=n=o.scrollLeft-s.scrollSpeed))):(s.axis&&"x"===s.axis||(t.pageY-V(a).scrollTop()<s.scrollSensitivity?n=V(a).scrollTop(V(a).scrollTop()-s.scrollSpeed):V(window).height()-(t.pageY-V(a).scrollTop())<s.scrollSensitivity&&(n=V(a).scrollTop(V(a).scrollTop()+s.scrollSpeed))),s.axis&&"y"===s.axis||(t.pageX-V(a).scrollLeft()<s.scrollSensitivity?n=V(a).scrollLeft(V(a).scrollLeft()-s.scrollSpeed):V(window).width()-(t.pageX-V(a).scrollLeft())<s.scrollSensitivity&&(n=V(a).scrollLeft(V(a).scrollLeft()+s.scrollSpeed)))),!1!==n&&V.ui.ddmanager&&!s.dropBehaviour&&V.ui.ddmanager.prepareOffsets(i,t)}}),V.ui.plugin.add("draggable","snap",{start:function(t,e,i){var s=i.options;i.snapElements=[],V(s.snap.constructor!==String?s.snap.items||":data(ui-draggable)":s.snap).each(function(){var t=V(this),e=t.offset();this!==i.element[0]&&i.snapElements.push({item:this,width:t.outerWidth(),height:t.outerHeight(),top:e.top,left:e.left})})},drag:function(t,e,i){for(var s,n,o,a,r,l,h,c,u,d=i.options,p=d.snapTolerance,f=e.offset.left,g=f+i.helperProportions.width,m=e.offset.top,_=m+i.helperProportions.height,v=i.snapElements.length-1;0<=v;v--)l=(r=i.snapElements[v].left-i.margins.left)+i.snapElements[v].width,c=(h=i.snapElements[v].top-i.margins.top)+i.snapElements[v].height,g<r-p||l+p<f||_<h-p||c+p<m||!V.contains(i.snapElements[v].item.ownerDocument,i.snapElements[v].item)?(i.snapElements[v].snapping&&i.options.snap.release&&i.options.snap.release.call(i.element,t,V.extend(i._uiHash(),{snapItem:i.snapElements[v].item})),i.snapElements[v].snapping=!1):("inner"!==d.snapMode&&(s=Math.abs(h-_)<=p,n=Math.abs(c-m)<=p,o=Math.abs(r-g)<=p,a=Math.abs(l-f)<=p,s&&(e.position.top=i._convertPositionTo("relative",{top:h-i.helperProportions.height,left:0}).top),n&&(e.position.top=i._convertPositionTo("relative",{top:c,left:0}).top),o&&(e.position.left=i._convertPositionTo("relative",{top:0,left:r-i.helperProportions.width}).left),a&&(e.position.left=i._convertPositionTo("relative",{top:0,left:l}).left)),u=s||n||o||a,"outer"!==d.snapMode&&(s=Math.abs(h-m)<=p,n=Math.abs(c-_)<=p,o=Math.abs(r-f)<=p,a=Math.abs(l-g)<=p,s&&(e.position.top=i._convertPositionTo("relative",{top:h,left:0}).top),n&&(e.position.top=i._convertPositionTo("relative",{top:c-i.helperProportions.height,left:0}).top),o&&(e.position.left=i._convertPositionTo("relative",{top:0,left:r}).left),a&&(e.position.left=i._convertPositionTo("relative",{top:0,left:l-i.helperProportions.width}).left)),!i.snapElements[v].snapping&&(s||n||o||a||u)&&i.options.snap.snap&&i.options.snap.snap.call(i.element,t,V.extend(i._uiHash(),{snapItem:i.snapElements[v].item})),i.snapElements[v].snapping=s||n||o||a||u)}}),V.ui.plugin.add("draggable","stack",{start:function(t,e,i){var s,i=i.options,i=V.makeArray(V(i.stack)).sort(function(t,e){return(parseInt(V(t).css("zIndex"),10)||0)-(parseInt(V(e).css("zIndex"),10)||0)});i.length&&(s=parseInt(V(i[0]).css("zIndex"),10)||0,V(i).each(function(t){V(this).css("zIndex",s+t)}),this.css("zIndex",s+i.length))}}),V.ui.plugin.add("draggable","zIndex",{start:function(t,e,i){e=V(e.helper),i=i.options;e.css("zIndex")&&(i._zIndex=e.css("zIndex")),e.css("zIndex",i.zIndex)},stop:function(t,e,i){i=i.options;i._zIndex&&V(e.helper).css("zIndex",i._zIndex)}});V.ui.draggable;V.widget("ui.resizable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"resize",options:{alsoResize:!1,animate:!1,animateDuration:"slow",animateEasing:"swing",aspectRatio:!1,autoHide:!1,classes:{"ui-resizable-se":"ui-icon ui-icon-gripsmall-diagonal-se"},containment:!1,ghost:!1,grid:!1,handles:"e,s,se",helper:!1,maxHeight:null,maxWidth:null,minHeight:10,minWidth:10,zIndex:90,resize:null,start:null,stop:null},_num:function(t){return parseFloat(t)||0},_isNumber:function(t){return!isNaN(parseFloat(t))},_hasScroll:function(t,e){if("hidden"===V(t).css("overflow"))return!1;var i=e&&"left"===e?"scrollLeft":"scrollTop",e=!1;if(0<t[i])return!0;try{t[i]=1,e=0<t[i],t[i]=0}catch(t){}return e},_create:function(){var t,e=this.options,i=this;this._addClass("ui-resizable"),V.extend(this,{_aspectRatio:!!e.aspectRatio,aspectRatio:e.aspectRatio,originalElement:this.element,_proportionallyResizeElements:[],_helper:e.helper||e.ghost||e.animate?e.helper||"ui-resizable-helper":null}),this.element[0].nodeName.match(/^(canvas|textarea|input|select|button|img)$/i)&&(this.element.wrap(V("<div class='ui-wrapper'></div>").css({overflow:"hidden",position:this.element.css("position"),width:this.element.outerWidth(),height:this.element.outerHeight(),top:this.element.css("top"),left:this.element.css("left")})),this.element=this.element.parent().data("ui-resizable",this.element.resizable("instance")),this.elementIsWrapper=!0,t={marginTop:this.originalElement.css("marginTop"),marginRight:this.originalElement.css("marginRight"),marginBottom:this.originalElement.css("marginBottom"),marginLeft:this.originalElement.css("marginLeft")},this.element.css(t),this.originalElement.css("margin",0),this.originalResizeStyle=this.originalElement.css("resize"),this.originalElement.css("resize","none"),this._proportionallyResizeElements.push(this.originalElement.css({position:"static",zoom:1,display:"block"})),this.originalElement.css(t),this._proportionallyResize()),this._setupHandles(),e.autoHide&&V(this.element).on("mouseenter",function(){e.disabled||(i._removeClass("ui-resizable-autohide"),i._handles.show())}).on("mouseleave",function(){e.disabled||i.resizing||(i._addClass("ui-resizable-autohide"),i._handles.hide())}),this._mouseInit()},_destroy:function(){this._mouseDestroy(),this._addedHandles.remove();function t(t){V(t).removeData("resizable").removeData("ui-resizable").off(".resizable")}var e;return this.elementIsWrapper&&(t(this.element),e=this.element,this.originalElement.css({position:e.css("position"),width:e.outerWidth(),height:e.outerHeight(),top:e.css("top"),left:e.css("left")}).insertAfter(e),e.remove()),this.originalElement.css("resize",this.originalResizeStyle),t(this.originalElement),this},_setOption:function(t,e){switch(this._super(t,e),t){case"handles":this._removeHandles(),this._setupHandles();break;case"aspectRatio":this._aspectRatio=!!e}},_setupHandles:function(){var t,e,i,s,n,o=this.options,a=this;if(this.handles=o.handles||(V(".ui-resizable-handle",this.element).length?{n:".ui-resizable-n",e:".ui-resizable-e",s:".ui-resizable-s",w:".ui-resizable-w",se:".ui-resizable-se",sw:".ui-resizable-sw",ne:".ui-resizable-ne",nw:".ui-resizable-nw"}:"e,s,se"),this._handles=V(),this._addedHandles=V(),this.handles.constructor===String)for("all"===this.handles&&(this.handles="n,e,s,w,se,sw,ne,nw"),i=this.handles.split(","),this.handles={},e=0;e<i.length;e++)s="ui-resizable-"+(t=String.prototype.trim.call(i[e])),n=V("<div>"),this._addClass(n,"ui-resizable-handle "+s),n.css({zIndex:o.zIndex}),this.handles[t]=".ui-resizable-"+t,this.element.children(this.handles[t]).length||(this.element.append(n),this._addedHandles=this._addedHandles.add(n));this._renderAxis=function(t){var e,i,s;for(e in t=t||this.element,this.handles)this.handles[e].constructor===String?this.handles[e]=this.element.children(this.handles[e]).first().show():(this.handles[e].jquery||this.handles[e].nodeType)&&(this.handles[e]=V(this.handles[e]),this._on(this.handles[e],{mousedown:a._mouseDown})),this.elementIsWrapper&&this.originalElement[0].nodeName.match(/^(textarea|input|select|button)$/i)&&(i=V(this.handles[e],this.element),s=/sw|ne|nw|se|n|s/.test(e)?i.outerHeight():i.outerWidth(),i=["padding",/ne|nw|n/.test(e)?"Top":/se|sw|s/.test(e)?"Bottom":/^e$/.test(e)?"Right":"Left"].join(""),t.css(i,s),this._proportionallyResize()),this._handles=this._handles.add(this.handles[e])},this._renderAxis(this.element),this._handles=this._handles.add(this.element.find(".ui-resizable-handle")),this._handles.disableSelection(),this._handles.on("mouseover",function(){a.resizing||(this.className&&(n=this.className.match(/ui-resizable-(se|sw|ne|nw|n|e|s|w)/i)),a.axis=n&&n[1]?n[1]:"se")}),o.autoHide&&(this._handles.hide(),this._addClass("ui-resizable-autohide"))},_removeHandles:function(){this._addedHandles.remove()},_mouseCapture:function(t){var e,i,s=!1;for(e in this.handles)(i=V(this.handles[e])[0])!==t.target&&!V.contains(i,t.target)||(s=!0);return!this.options.disabled&&s},_mouseStart:function(t){var e,i,s=this.options,n=this.element;return this.resizing=!0,this._renderProxy(),e=this._num(this.helper.css("left")),i=this._num(this.helper.css("top")),s.containment&&(e+=V(s.containment).scrollLeft()||0,i+=V(s.containment).scrollTop()||0),this.offset=this.helper.offset(),this.position={left:e,top:i},this.size=this._helper?{width:this.helper.width(),height:this.helper.height()}:{width:n.width(),height:n.height()},this.originalSize=this._helper?{width:n.outerWidth(),height:n.outerHeight()}:{width:n.width(),height:n.height()},this.sizeDiff={width:n.outerWidth()-n.width(),height:n.outerHeight()-n.height()},this.originalPosition={left:e,top:i},this.originalMousePosition={left:t.pageX,top:t.pageY},this.aspectRatio="number"==typeof s.aspectRatio?s.aspectRatio:this.originalSize.width/this.originalSize.height||1,s=V(".ui-resizable-"+this.axis).css("cursor"),V("body").css("cursor","auto"===s?this.axis+"-resize":s),this._addClass("ui-resizable-resizing"),this._propagate("start",t),!0},_mouseDrag:function(t){var e=this.originalMousePosition,i=this.axis,s=t.pageX-e.left||0,e=t.pageY-e.top||0,i=this._change[i];return this._updatePrevProperties(),i&&(e=i.apply(this,[t,s,e]),this._updateVirtualBoundaries(t.shiftKey),(this._aspectRatio||t.shiftKey)&&(e=this._updateRatio(e,t)),e=this._respectSize(e,t),this._updateCache(e),this._propagate("resize",t),e=this._applyChanges(),!this._helper&&this._proportionallyResizeElements.length&&this._proportionallyResize(),V.isEmptyObject(e)||(this._updatePrevProperties(),this._trigger("resize",t,this.ui()),this._applyChanges())),!1},_mouseStop:function(t){this.resizing=!1;var e,i,s,n=this.options,o=this;return this._helper&&(s=(e=(i=this._proportionallyResizeElements).length&&/textarea/i.test(i[0].nodeName))&&this._hasScroll(i[0],"left")?0:o.sizeDiff.height,i=e?0:o.sizeDiff.width,e={width:o.helper.width()-i,height:o.helper.height()-s},i=parseFloat(o.element.css("left"))+(o.position.left-o.originalPosition.left)||null,s=parseFloat(o.element.css("top"))+(o.position.top-o.originalPosition.top)||null,n.animate||this.element.css(V.extend(e,{top:s,left:i})),o.helper.height(o.size.height),o.helper.width(o.size.width),this._helper&&!n.animate&&this._proportionallyResize()),V("body").css("cursor","auto"),this._removeClass("ui-resizable-resizing"),this._propagate("stop",t),this._helper&&this.helper.remove(),!1},_updatePrevProperties:function(){this.prevPosition={top:this.position.top,left:this.position.left},this.prevSize={width:this.size.width,height:this.size.height}},_applyChanges:function(){var t={};return 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t=this.options.appendTo;return t&&(t.jquery||t.nodeType)?V(t):this.document.find(t||"body").eq(0)},_destroy:function(){var t,e=this.originalPosition;this._untrackInstance(),this._destroyOverlay(),this.element.removeUniqueId().css(this.originalCss).detach(),this.uiDialog.remove(),this.originalTitle&&this.element.attr("title",this.originalTitle),(t=e.parent.children().eq(e.index)).length&&t[0]!==this.element[0]?t.before(this.element):e.parent.append(this.element)},widget:function(){return this.uiDialog},disable:V.noop,enable:V.noop,close:function(t){var e=this;this._isOpen&&!1!==this._trigger("beforeClose",t)&&(this._isOpen=!1,this._focusedElement=null,this._destroyOverlay(),this._untrackInstance(),this.opener.filter(":focusable").trigger("focus").length||V.ui.safeBlur(V.ui.safeActiveElement(this.document[0])),this._hide(this.uiDialog,this.options.hide,function(){e._trigger("close",t)}))},isOpen:function(){return this._isOpen},moveToTop:function(){this._moveToTop()},_moveToTop:function(t,e){var i=!1,s=this.uiDialog.siblings(".ui-front:visible").map(function(){return+V(this).css("z-index")}).get(),s=Math.max.apply(null,s);return s>=+this.uiDialog.css("z-index")&&(this.uiDialog.css("z-index",s+1),i=!0),i&&!e&&this._trigger("focus",t),i},open:function(){var t=this;this._isOpen?this._moveToTop()&&this._focusTabbable():(this._isOpen=!0,this.opener=V(V.ui.safeActiveElement(this.document[0])),this._size(),this._position(),this._createOverlay(),this._moveToTop(null,!0),this.overlay&&this.overlay.css("z-index",this.uiDialog.css("z-index")-1),this._show(this.uiDialog,this.options.show,function(){t._focusTabbable(),t._trigger("focus")}),this._makeFocusTarget(),this._trigger("open"))},_focusTabbable:function(){var t=this._focusedElement;(t=!(t=!(t=!(t=!(t=t||this.element.find("[autofocus]")).length?this.element.find(":tabbable"):t).length?this.uiDialogButtonPane.find(":tabbable"):t).length?this.uiDialogTitlebarClose.filter(":tabbable"):t).length?this.uiDialog:t).eq(0).trigger("focus")},_restoreTabbableFocus:function(){var t=V.ui.safeActiveElement(this.document[0]);this.uiDialog[0]===t||V.contains(this.uiDialog[0],t)||this._focusTabbable()},_keepFocus:function(t){t.preventDefault(),this._restoreTabbableFocus(),this._delay(this._restoreTabbableFocus)},_createWrapper:function(){this.uiDialog=V("<div>").hide().attr({tabIndex:-1,role:"dialog"}).appendTo(this._appendTo()),this._addClass(this.uiDialog,"ui-dialog","ui-widget ui-widget-content ui-front"),this._on(this.uiDialog,{keydown:function(t){if(this.options.closeOnEscape&&!t.isDefaultPrevented()&&t.keyCode&&t.keyCode===V.ui.keyCode.ESCAPE)return t.preventDefault(),void this.close(t);var e,i,s;t.keyCode!==V.ui.keyCode.TAB||t.isDefaultPrevented()||(e=this.uiDialog.find(":tabbable"),i=e.first(),s=e.last(),t.target!==s[0]&&t.target!==this.uiDialog[0]||t.shiftKey?t.target!==i[0]&&t.target!==this.uiDialog[0]||!t.shiftKey||(this._delay(function(){s.trigger("focus")}),t.preventDefault()):(this._delay(function(){i.trigger("focus")}),t.preventDefault()))},mousedown:function(t){this._moveToTop(t)&&this._focusTabbable()}}),this.element.find("[aria-describedby]").length||this.uiDialog.attr({"aria-describedby":this.element.uniqueId().attr("id")})},_createTitlebar:function(){var t;this.uiDialogTitlebar=V("<div>"),this._addClass(this.uiDialogTitlebar,"ui-dialog-titlebar","ui-widget-header ui-helper-clearfix"),this._on(this.uiDialogTitlebar,{mousedown:function(t){V(t.target).closest(".ui-dialog-titlebar-close")||this.uiDialog.trigger("focus")}}),this.uiDialogTitlebarClose=V("<button 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s=this,t=this.options.buttons;this.uiDialogButtonPane.remove(),this.uiButtonSet.empty(),V.isEmptyObject(t)||Array.isArray(t)&&!t.length?this._removeClass(this.uiDialog,"ui-dialog-buttons"):(V.each(t,function(t,e){var i;e=V.extend({type:"button"},e="function"==typeof e?{click:e,text:t}:e),i=e.click,t={icon:e.icon,iconPosition:e.iconPosition,showLabel:e.showLabel,icons:e.icons,text:e.text},delete e.click,delete e.icon,delete e.iconPosition,delete e.showLabel,delete e.icons,"boolean"==typeof e.text&&delete e.text,V("<button></button>",e).button(t).appendTo(s.uiButtonSet).on("click",function(){i.apply(s.element[0],arguments)})}),this._addClass(this.uiDialog,"ui-dialog-buttons"),this.uiDialogButtonPane.appendTo(this.uiDialog))},_makeDraggable:function(){var n=this,o=this.options;function a(t){return{position:t.position,offset:t.offset}}this.uiDialog.draggable({cancel:".ui-dialog-content, .ui-dialog-titlebar-close",handle:".ui-dialog-titlebar",containment:"document",start:function(t,e){n._addClass(V(this),"ui-dialog-dragging"),n._blockFrames(),n._trigger("dragStart",t,a(e))},drag:function(t,e){n._trigger("drag",t,a(e))},stop:function(t,e){var i=e.offset.left-n.document.scrollLeft(),s=e.offset.top-n.document.scrollTop();o.position={my:"left top",at:"left"+(0<=i?"+":"")+i+" top"+(0<=s?"+":"")+s,of:n.window},n._removeClass(V(this),"ui-dialog-dragging"),n._unblockFrames(),n._trigger("dragStop",t,a(e))}})},_makeResizable:function(){var n=this,o=this.options,t=o.resizable,e=this.uiDialog.css("position"),t="string"==typeof t?t:"n,e,s,w,se,sw,ne,nw";function a(t){return{originalPosition:t.originalPosition,originalSize:t.originalSize,position:t.position,size:t.size}}this.uiDialog.resizable({cancel:".ui-dialog-content",containment:"document",alsoResize:this.element,maxWidth:o.maxWidth,maxHeight:o.maxHeight,minWidth:o.minWidth,minHeight:this._minHeight(),handles:t,start:function(t,e){n._addClass(V(this),"ui-dialog-resizing"),n._blockFrames(),n._trigger("resizeStart",t,a(e))},resize:function(t,e){n._trigger("resize",t,a(e))},stop:function(t,e){var i=n.uiDialog.offset(),s=i.left-n.document.scrollLeft(),i=i.top-n.document.scrollTop();o.height=n.uiDialog.height(),o.width=n.uiDialog.width(),o.position={my:"left top",at:"left"+(0<=s?"+":"")+s+" top"+(0<=i?"+":"")+i,of:n.window},n._removeClass(V(this),"ui-dialog-resizing"),n._unblockFrames(),n._trigger("resizeStop",t,a(e))}}).css("position",e)},_trackFocus:function(){this._on(this.widget(),{focusin:function(t){this._makeFocusTarget(),this._focusedElement=V(t.target)}})},_makeFocusTarget:function(){this._untrackInstance(),this._trackingInstances().unshift(this)},_untrackInstance:function(){var t=this._trackingInstances(),e=V.inArray(this,t);-1!==e&&t.splice(e,1)},_trackingInstances:function(){var t=this.document.data("ui-dialog-instances");return t||this.document.data("ui-dialog-instances",t=[]),t},_minHeight:function(){var t=this.options;return"auto"===t.height?t.minHeight:Math.min(t.minHeight,t.height)},_position:function(){var t=this.uiDialog.is(":visible");t||this.uiDialog.show(),this.uiDialog.position(this.options.position),t||this.uiDialog.hide()},_setOptions:function(t){var i=this,s=!1,n={};V.each(t,function(t,e){i._setOption(t,e),t in i.sizeRelatedOptions&&(s=!0),t in i.resizableRelatedOptions&&(n[t]=e)}),s&&(this._size(),this._position()),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option",n)},_setOption:function(t,e){var i,s=this.uiDialog;"disabled"!==t&&(this._super(t,e),"appendTo"===t&&this.uiDialog.appendTo(this._appendTo()),"buttons"===t&&this._createButtons(),"closeText"===t&&this.uiDialogTitlebarClose.button({label:V("<a>").text(""+this.options.closeText).html()}),"draggable"===t&&((i=s.is(":data(ui-draggable)"))&&!e&&s.draggable("destroy"),!i&&e&&this._makeDraggable()),"position"===t&&this._position(),"resizable"===t&&((i=s.is(":data(ui-resizable)"))&&!e&&s.resizable("destroy"),i&&"string"==typeof e&&s.resizable("option","handles",e),i||!1===e||this._makeResizable()),"title"===t&&this._title(this.uiDialogTitlebar.find(".ui-dialog-title")))},_size:function(){var t,e,i,s=this.options;this.element.show().css({width:"auto",minHeight:0,maxHeight:"none",height:0}),s.minWidth>s.width&&(s.width=s.minWidth),t=this.uiDialog.css({height:"auto",width:s.width}).outerHeight(),e=Math.max(0,s.minHeight-t),i="number"==typeof s.maxHeight?Math.max(0,s.maxHeight-t):"none","auto"===s.height?this.element.css({minHeight:e,maxHeight:i,height:"auto"}):this.element.height(Math.max(0,s.height-t)),this.uiDialog.is(":data(ui-resizable)")&&this.uiDialog.resizable("option","minHeight",this._minHeight())},_blockFrames:function(){this.iframeBlocks=this.document.find("iframe").map(function(){var t=V(this);return V("<div>").css({position:"absolute",width:t.outerWidth(),height:t.outerHeight()}).appendTo(t.parent()).offset(t.offset())[0]})},_unblockFrames:function(){this.iframeBlocks&&(this.iframeBlocks.remove(),delete this.iframeBlocks)},_allowInteraction:function(t){return!!V(t.target).closest(".ui-dialog").length||!!V(t.target).closest(".ui-datepicker").length},_createOverlay:function(){var i,s;this.options.modal&&(i=V.fn.jquery.substring(0,4),s=!0,this._delay(function(){s=!1}),this.document.data("ui-dialog-overlays")||this.document.on("focusin.ui-dialog",function(t){var e;s||((e=this._trackingInstances()[0])._allowInteraction(t)||(t.preventDefault(),e._focusTabbable(),"3.4."!==i&&"3.5."!==i||e._delay(e._restoreTabbableFocus)))}.bind(this)),this.overlay=V("<div>").appendTo(this._appendTo()),this._addClass(this.overlay,null,"ui-widget-overlay ui-front"),this._on(this.overlay,{mousedown:"_keepFocus"}),this.document.data("ui-dialog-overlays",(this.document.data("ui-dialog-overlays")||0)+1))},_destroyOverlay:function(){var t;this.options.modal&&this.overlay&&((t=this.document.data("ui-dialog-overlays")-1)?this.document.data("ui-dialog-overlays",t):(this.document.off("focusin.ui-dialog"),this.document.removeData("ui-dialog-overlays")),this.overlay.remove(),this.overlay=null)}}),!1!==V.uiBackCompat&&V.widget("ui.dialog",V.ui.dialog,{options:{dialogClass:""},_createWrapper:function(){this._super(),this.uiDialog.addClass(this.options.dialogClass)},_setOption:function(t,e){"dialogClass"===t&&this.uiDialog.removeClass(this.options.dialogClass).addClass(e),this._superApply(arguments)}});V.ui.dialog;function lt(t,e,i){return e<=t&&t<e+i}V.widget("ui.droppable",{version:"1.13.2",widgetEventPrefix:"drop",options:{accept:"*",addClasses:!0,greedy:!1,scope:"default",tolerance:"intersect",activate:null,deactivate:null,drop:null,out:null,over:null},_create:function(){var t,e=this.options,i=e.accept;this.isover=!1,this.isout=!0,this.accept="function"==typeof i?i:function(t){return t.is(i)},this.proportions=function(){if(!arguments.length)return t=t||{width:this.element[0].offsetWidth,height:this.element[0].offsetHeight};t=arguments[0]},this._addToManager(e.scope),e.addClasses&&this._addClass("ui-droppable")},_addToManager:function(t){V.ui.ddmanager.droppables[t]=V.ui.ddmanager.droppables[t]||[],V.ui.ddmanager.droppables[t].push(this)},_splice:function(t){for(var e=0;e<t.length;e++)t[e]===this&&t.splice(e,1)},_destroy:function(){var t=V.ui.ddmanager.droppables[this.options.scope];this._splice(t)},_setOption:function(t,e){var i;"accept"===t?this.accept="function"==typeof e?e:function(t){return t.is(e)}:"scope"===t&&(i=V.ui.ddmanager.droppables[this.options.scope],this._splice(i),this._addToManager(e)),this._super(t,e)},_activate:function(t){var e=V.ui.ddmanager.current;this._addActiveClass(),e&&this._trigger("activate",t,this.ui(e))},_deactivate:function(t){var e=V.ui.ddmanager.current;this._removeActiveClass(),e&&this._trigger("deactivate",t,this.ui(e))},_over:function(t){var e=V.ui.ddmanager.current;e&&(e.currentItem||e.element)[0]!==this.element[0]&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this._addHoverClass(),this._trigger("over",t,this.ui(e)))},_out:function(t){var e=V.ui.ddmanager.current;e&&(e.currentItem||e.element)[0]!==this.element[0]&&this.accept.call(this.element[0],e.currentItem||e.element)&&(this._removeHoverClass(),this._trigger("out",t,this.ui(e)))},_drop:function(e,t){var i=t||V.ui.ddmanager.current,s=!1;return!(!i||(i.currentItem||i.element)[0]===this.element[0])&&(this.element.find(":data(ui-droppable)").not(".ui-draggable-dragging").each(function(){var t=V(this).droppable("instance");if(t.options.greedy&&!t.options.disabled&&t.options.scope===i.options.scope&&t.accept.call(t.element[0],i.currentItem||i.element)&&V.ui.intersect(i,V.extend(t,{offset:t.element.offset()}),t.options.tolerance,e))return!(s=!0)}),!s&&(!!this.accept.call(this.element[0],i.currentItem||i.element)&&(this._removeActiveClass(),this._removeHoverClass(),this._trigger("drop",e,this.ui(i)),this.element)))},ui:function(t){return{draggable:t.currentItem||t.element,helper:t.helper,position:t.position,offset:t.positionAbs}},_addHoverClass:function(){this._addClass("ui-droppable-hover")},_removeHoverClass:function(){this._removeClass("ui-droppable-hover")},_addActiveClass:function(){this._addClass("ui-droppable-active")},_removeActiveClass:function(){this._removeClass("ui-droppable-active")}}),V.ui.intersect=function(t,e,i,s){if(!e.offset)return!1;var n=(t.positionAbs||t.position.absolute).left+t.margins.left,o=(t.positionAbs||t.position.absolute).top+t.margins.top,a=n+t.helperProportions.width,r=o+t.helperProportions.height,l=e.offset.left,h=e.offset.top,c=l+e.proportions().width,u=h+e.proportions().height;switch(i){case"fit":return l<=n&&a<=c&&h<=o&&r<=u;case"intersect":return l<n+t.helperProportions.width/2&&a-t.helperProportions.width/2<c&&h<o+t.helperProportions.height/2&&r-t.helperProportions.height/2<u;case"pointer":return lt(s.pageY,h,e.proportions().height)&&lt(s.pageX,l,e.proportions().width);case"touch":return(h<=o&&o<=u||h<=r&&r<=u||o<h&&u<r)&&(l<=n&&n<=c||l<=a&&a<=c||n<l&&c<a);default:return!1}},!(V.ui.ddmanager={current:null,droppables:{default:[]},prepareOffsets:function(t,e){var i,s,n=V.ui.ddmanager.droppables[t.options.scope]||[],o=e?e.type:null,a=(t.currentItem||t.element).find(":data(ui-droppable)").addBack();t:for(i=0;i<n.length;i++)if(!(n[i].options.disabled||t&&!n[i].accept.call(n[i].element[0],t.currentItem||t.element))){for(s=0;s<a.length;s++)if(a[s]===n[i].element[0]){n[i].proportions().height=0;continue t}n[i].visible="none"!==n[i].element.css("display"),n[i].visible&&("mousedown"===o&&n[i]._activate.call(n[i],e),n[i].offset=n[i].element.offset(),n[i].proportions({width:n[i].element[0].offsetWidth,height:n[i].element[0].offsetHeight}))}},drop:function(t,e){var i=!1;return V.each((V.ui.ddmanager.droppables[t.options.scope]||[]).slice(),function(){this.options&&(!this.options.disabled&&this.visible&&V.ui.intersect(t,this,this.options.tolerance,e)&&(i=this._drop.call(this,e)||i),!this.options.disabled&&this.visible&&this.accept.call(this.element[0],t.currentItem||t.element)&&(this.isout=!0,this.isover=!1,this._deactivate.call(this,e)))}),i},dragStart:function(t,e){t.element.parentsUntil("body").on("scroll.droppable",function(){t.options.refreshPositions||V.ui.ddmanager.prepareOffsets(t,e)})},drag:function(n,o){n.options.refreshPositions&&V.ui.ddmanager.prepareOffsets(n,o),V.each(V.ui.ddmanager.droppables[n.options.scope]||[],function(){var t,e,i,s;this.options.disabled||this.greedyChild||!this.visible||(s=!(i=V.ui.intersect(n,this,this.options.tolerance,o))&&this.isover?"isout":i&&!this.isover?"isover":null)&&(this.options.greedy&&(e=this.options.scope,(i=this.element.parents(":data(ui-droppable)").filter(function(){return V(this).droppable("instance").options.scope===e})).length&&((t=V(i[0]).droppable("instance")).greedyChild="isover"===s)),t&&"isover"===s&&(t.isover=!1,t.isout=!0,t._out.call(t,o)),this[s]=!0,this["isout"===s?"isover":"isout"]=!1,this["isover"===s?"_over":"_out"].call(this,o),t&&"isout"===s&&(t.isout=!1,t.isover=!0,t._over.call(t,o)))})},dragStop:function(t,e){t.element.parentsUntil("body").off("scroll.droppable"),t.options.refreshPositions||V.ui.ddmanager.prepareOffsets(t,e)}})!==V.uiBackCompat&&V.widget("ui.droppable",V.ui.droppable,{options:{hoverClass:!1,activeClass:!1},_addActiveClass:function(){this._super(),this.options.activeClass&&this.element.addClass(this.options.activeClass)},_removeActiveClass:function(){this._super(),this.options.activeClass&&this.element.removeClass(this.options.activeClass)},_addHoverClass:function(){this._super(),this.options.hoverClass&&this.element.addClass(this.options.hoverClass)},_removeHoverClass:function(){this._super(),this.options.hoverClass&&this.element.removeClass(this.options.hoverClass)}});V.ui.droppable,V.widget("ui.progressbar",{version:"1.13.2",options:{classes:{"ui-progressbar":"ui-corner-all","ui-progressbar-value":"ui-corner-left","ui-progressbar-complete":"ui-corner-right"},max:100,value:0,change:null,complete:null},min:0,_create:function(){this.oldValue=this.options.value=this._constrainedValue(),this.element.attr({role:"progressbar","aria-valuemin":this.min}),this._addClass("ui-progressbar","ui-widget ui-widget-content"),this.valueDiv=V("<div>").appendTo(this.element),this._addClass(this.valueDiv,"ui-progressbar-value","ui-widget-header"),this._refreshValue()},_destroy:function(){this.element.removeAttr("role aria-valuemin aria-valuemax aria-valuenow"),this.valueDiv.remove()},value:function(t){if(void 0===t)return this.options.value;this.options.value=this._constrainedValue(t),this._refreshValue()},_constrainedValue:function(t){return void 0===t&&(t=this.options.value),this.indeterminate=!1===t,"number"!=typeof t&&(t=0),!this.indeterminate&&Math.min(this.options.max,Math.max(this.min,t))},_setOptions:function(t){var e=t.value;delete t.value,this._super(t),this.options.value=this._constrainedValue(e),this._refreshValue()},_setOption:function(t,e){"max"===t&&(e=Math.max(this.min,e)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this.element.attr("aria-disabled",t),this._toggleClass(null,"ui-state-disabled",!!t)},_percentage:function(){return this.indeterminate?100:100*(this.options.value-this.min)/(this.options.max-this.min)},_refreshValue:function(){var t=this.options.value,e=this._percentage();this.valueDiv.toggle(this.indeterminate||t>this.min).width(e.toFixed(0)+"%"),this._toggleClass(this.valueDiv,"ui-progressbar-complete",null,t===this.options.max)._toggleClass("ui-progressbar-indeterminate",null,this.indeterminate),this.indeterminate?(this.element.removeAttr("aria-valuenow"),this.overlayDiv||(this.overlayDiv=V("<div>").appendTo(this.valueDiv),this._addClass(this.overlayDiv,"ui-progressbar-overlay"))):(this.element.attr({"aria-valuemax":this.options.max,"aria-valuenow":t}),this.overlayDiv&&(this.overlayDiv.remove(),this.overlayDiv=null)),this.oldValue!==t&&(this.oldValue=t,this._trigger("change")),t===this.options.max&&this._trigger("complete")}}),V.widget("ui.selectable",V.ui.mouse,{version:"1.13.2",options:{appendTo:"body",autoRefresh:!0,distance:0,filter:"*",tolerance:"touch",selected:null,selecting:null,start:null,stop:null,unselected:null,unselecting:null},_create:function(){var i=this;this._addClass("ui-selectable"),this.dragged=!1,this.refresh=function(){i.elementPos=V(i.element[0]).offset(),i.selectees=V(i.options.filter,i.element[0]),i._addClass(i.selectees,"ui-selectee"),i.selectees.each(function(){var t=V(this),e=t.offset(),e={left:e.left-i.elementPos.left,top:e.top-i.elementPos.top};V.data(this,"selectable-item",{element:this,$element:t,left:e.left,top:e.top,right:e.left+t.outerWidth(),bottom:e.top+t.outerHeight(),startselected:!1,selected:t.hasClass("ui-selected"),selecting:t.hasClass("ui-selecting"),unselecting:t.hasClass("ui-unselecting")})})},this.refresh(),this._mouseInit(),this.helper=V("<div>"),this._addClass(this.helper,"ui-selectable-helper")},_destroy:function(){this.selectees.removeData("selectable-item"),this._mouseDestroy()},_mouseStart:function(i){var s=this,t=this.options;this.opos=[i.pageX,i.pageY],this.elementPos=V(this.element[0]).offset(),this.options.disabled||(this.selectees=V(t.filter,this.element[0]),this._trigger("start",i),V(t.appendTo).append(this.helper),this.helper.css({left:i.pageX,top:i.pageY,width:0,height:0}),t.autoRefresh&&this.refresh(),this.selectees.filter(".ui-selected").each(function(){var t=V.data(this,"selectable-item");t.startselected=!0,i.metaKey||i.ctrlKey||(s._removeClass(t.$element,"ui-selected"),t.selected=!1,s._addClass(t.$element,"ui-unselecting"),t.unselecting=!0,s._trigger("unselecting",i,{unselecting:t.element}))}),V(i.target).parents().addBack().each(function(){var t,e=V.data(this,"selectable-item");if(e)return t=!i.metaKey&&!i.ctrlKey||!e.$element.hasClass("ui-selected"),s._removeClass(e.$element,t?"ui-unselecting":"ui-selected")._addClass(e.$element,t?"ui-selecting":"ui-unselecting"),e.unselecting=!t,e.selecting=t,(e.selected=t)?s._trigger("selecting",i,{selecting:e.element}):s._trigger("unselecting",i,{unselecting:e.element}),!1}))},_mouseDrag:function(s){if(this.dragged=!0,!this.options.disabled){var t,n=this,o=this.options,a=this.opos[0],r=this.opos[1],l=s.pageX,h=s.pageY;return l<a&&(t=l,l=a,a=t),h<r&&(t=h,h=r,r=t),this.helper.css({left:a,top:r,width:l-a,height:h-r}),this.selectees.each(function(){var t=V.data(this,"selectable-item"),e=!1,i={};t&&t.element!==n.element[0]&&(i.left=t.left+n.elementPos.left,i.right=t.right+n.elementPos.left,i.top=t.top+n.elementPos.top,i.bottom=t.bottom+n.elementPos.top,"touch"===o.tolerance?e=!(i.left>l||i.right<a||i.top>h||i.bottom<r):"fit"===o.tolerance&&(e=i.left>a&&i.right<l&&i.top>r&&i.bottom<h),e?(t.selected&&(n._removeClass(t.$element,"ui-selected"),t.selected=!1),t.unselecting&&(n._removeClass(t.$element,"ui-unselecting"),t.unselecting=!1),t.selecting||(n._addClass(t.$element,"ui-selecting"),t.selecting=!0,n._trigger("selecting",s,{selecting:t.element}))):(t.selecting&&((s.metaKey||s.ctrlKey)&&t.startselected?(n._removeClass(t.$element,"ui-selecting"),t.selecting=!1,n._addClass(t.$element,"ui-selected"),t.selected=!0):(n._removeClass(t.$element,"ui-selecting"),t.selecting=!1,t.startselected&&(n._addClass(t.$element,"ui-unselecting"),t.unselecting=!0),n._trigger("unselecting",s,{unselecting:t.element}))),t.selected&&(s.metaKey||s.ctrlKey||t.startselected||(n._removeClass(t.$element,"ui-selected"),t.selected=!1,n._addClass(t.$element,"ui-unselecting"),t.unselecting=!0,n._trigger("unselecting",s,{unselecting:t.element})))))}),!1}},_mouseStop:function(e){var i=this;return this.dragged=!1,V(".ui-unselecting",this.element[0]).each(function(){var t=V.data(this,"selectable-item");i._removeClass(t.$element,"ui-unselecting"),t.unselecting=!1,t.startselected=!1,i._trigger("unselected",e,{unselected:t.element})}),V(".ui-selecting",this.element[0]).each(function(){var t=V.data(this,"selectable-item");i._removeClass(t.$element,"ui-selecting")._addClass(t.$element,"ui-selected"),t.selecting=!1,t.selected=!0,t.startselected=!0,i._trigger("selected",e,{selected:t.element})}),this._trigger("stop",e),this.helper.remove(),!1}}),V.widget("ui.selectmenu",[V.ui.formResetMixin,{version:"1.13.2",defaultElement:"<select>",options:{appendTo:null,classes:{"ui-selectmenu-button-open":"ui-corner-top","ui-selectmenu-button-closed":"ui-corner-all"},disabled:null,icons:{button:"ui-icon-triangle-1-s"},position:{my:"left top",at:"left bottom",collision:"none"},width:!1,change:null,close:null,focus:null,open:null,select:null},_create:function(){var t=this.element.uniqueId().attr("id");this.ids={element:t,button:t+"-button",menu:t+"-menu"},this._drawButton(),this._drawMenu(),this._bindFormResetHandler(),this._rendered=!1,this.menuItems=V()},_drawButton:function(){var t,e=this,i=this._parseOption(this.element.find("option:selected"),this.element[0].selectedIndex);this.labels=this.element.labels().attr("for",this.ids.button),this._on(this.labels,{click:function(t){this.button.trigger("focus"),t.preventDefault()}}),this.element.hide(),this.button=V("<span>",{tabindex:this.options.disabled?-1:0,id:this.ids.button,role:"combobox","aria-expanded":"false","aria-autocomplete":"list","aria-owns":this.ids.menu,"aria-haspopup":"true",title:this.element.attr("title")}).insertAfter(this.element),this._addClass(this.button,"ui-selectmenu-button ui-selectmenu-button-closed","ui-button ui-widget"),t=V("<span>").appendTo(this.button),this._addClass(t,"ui-selectmenu-icon","ui-icon "+this.options.icons.button),this.buttonItem=this._renderButtonItem(i).appendTo(this.button),!1!==this.options.width&&this._resizeButton(),this._on(this.button,this._buttonEvents),this.button.one("focusin",function(){e._rendered||e._refreshMenu()})},_drawMenu:function(){var i=this;this.menu=V("<ul>",{"aria-hidden":"true","aria-labelledby":this.ids.button,id:this.ids.menu}),this.menuWrap=V("<div>").append(this.menu),this._addClass(this.menuWrap,"ui-selectmenu-menu","ui-front"),this.menuWrap.appendTo(this._appendTo()),this.menuInstance=this.menu.menu({classes:{"ui-menu":"ui-corner-bottom"},role:"listbox",select:function(t,e){t.preventDefault(),i._setSelection(),i._select(e.item.data("ui-selectmenu-item"),t)},focus:function(t,e){e=e.item.data("ui-selectmenu-item");null!=i.focusIndex&&e.index!==i.focusIndex&&(i._trigger("focus",t,{item:e}),i.isOpen||i._select(e,t)),i.focusIndex=e.index,i.button.attr("aria-activedescendant",i.menuItems.eq(e.index).attr("id"))}}).menu("instance"),this.menuInstance._off(this.menu,"mouseleave"),this.menuInstance._closeOnDocumentClick=function(){return!1},this.menuInstance._isDivider=function(){return!1}},refresh:function(){this._refreshMenu(),this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(this._getSelectedItem().data("ui-selectmenu-item")||{})),null===this.options.width&&this._resizeButton()},_refreshMenu:function(){var t=this.element.find("option");this.menu.empty(),this._parseOptions(t),this._renderMenu(this.menu,this.items),this.menuInstance.refresh(),this.menuItems=this.menu.find("li").not(".ui-selectmenu-optgroup").find(".ui-menu-item-wrapper"),this._rendered=!0,t.length&&(t=this._getSelectedItem(),this.menuInstance.focus(null,t),this._setAria(t.data("ui-selectmenu-item")),this._setOption("disabled",this.element.prop("disabled")))},open:function(t){this.options.disabled||(this._rendered?(this._removeClass(this.menu.find(".ui-state-active"),null,"ui-state-active"),this.menuInstance.focus(null,this._getSelectedItem())):this._refreshMenu(),this.menuItems.length&&(this.isOpen=!0,this._toggleAttr(),this._resizeMenu(),this._position(),this._on(this.document,this._documentClick),this._trigger("open",t)))},_position:function(){this.menuWrap.position(V.extend({of:this.button},this.options.position))},close:function(t){this.isOpen&&(this.isOpen=!1,this._toggleAttr(),this.range=null,this._off(this.document),this._trigger("close",t))},widget:function(){return this.button},menuWidget:function(){return this.menu},_renderButtonItem:function(t){var e=V("<span>");return this._setText(e,t.label),this._addClass(e,"ui-selectmenu-text"),e},_renderMenu:function(s,t){var n=this,o="";V.each(t,function(t,e){var i;e.optgroup!==o&&(i=V("<li>",{text:e.optgroup}),n._addClass(i,"ui-selectmenu-optgroup","ui-menu-divider"+(e.element.parent("optgroup").prop("disabled")?" ui-state-disabled":"")),i.appendTo(s),o=e.optgroup),n._renderItemData(s,e)})},_renderItemData:function(t,e){return this._renderItem(t,e).data("ui-selectmenu-item",e)},_renderItem:function(t,e){var i=V("<li>"),s=V("<div>",{title:e.element.attr("title")});return e.disabled&&this._addClass(i,null,"ui-state-disabled"),this._setText(s,e.label),i.append(s).appendTo(t)},_setText:function(t,e){e?t.text(e):t.html("&#160;")},_move:function(t,e){var i,s=".ui-menu-item";this.isOpen?i=this.menuItems.eq(this.focusIndex).parent("li"):(i=this.menuItems.eq(this.element[0].selectedIndex).parent("li"),s+=":not(.ui-state-disabled)"),(s="first"===t||"last"===t?i["first"===t?"prevAll":"nextAll"](s).eq(-1):i[t+"All"](s).eq(0)).length&&this.menuInstance.focus(e,s)},_getSelectedItem:function(){return this.menuItems.eq(this.element[0].selectedIndex).parent("li")},_toggle:function(t){this[this.isOpen?"close":"open"](t)},_setSelection:function(){var t;this.range&&(window.getSelection?((t=window.getSelection()).removeAllRanges(),t.addRange(this.range)):this.range.select(),this.button.trigger("focus"))},_documentClick:{mousedown:function(t){this.isOpen&&(V(t.target).closest(".ui-selectmenu-menu, #"+V.escapeSelector(this.ids.button)).length||this.close(t))}},_buttonEvents:{mousedown:function(){var t;window.getSelection?(t=window.getSelection()).rangeCount&&(this.range=t.getRangeAt(0)):this.range=document.selection.createRange()},click:function(t){this._setSelection(),this._toggle(t)},keydown:function(t){var e=!0;switch(t.keyCode){case V.ui.keyCode.TAB:case V.ui.keyCode.ESCAPE:this.close(t),e=!1;break;case V.ui.keyCode.ENTER:this.isOpen&&this._selectFocusedItem(t);break;case V.ui.keyCode.UP:t.altKey?this._toggle(t):this._move("prev",t);break;case V.ui.keyCode.DOWN:t.altKey?this._toggle(t):this._move("next",t);break;case V.ui.keyCode.SPACE:this.isOpen?this._selectFocusedItem(t):this._toggle(t);break;case V.ui.keyCode.LEFT:this._move("prev",t);break;case V.ui.keyCode.RIGHT:this._move("next",t);break;case V.ui.keyCode.HOME:case V.ui.keyCode.PAGE_UP:this._move("first",t);break;case V.ui.keyCode.END:case V.ui.keyCode.PAGE_DOWN:this._move("last",t);break;default:this.menu.trigger(t),e=!1}e&&t.preventDefault()}},_selectFocusedItem:function(t){var e=this.menuItems.eq(this.focusIndex).parent("li");e.hasClass("ui-state-disabled")||this._select(e.data("ui-selectmenu-item"),t)},_select:function(t,e){var i=this.element[0].selectedIndex;this.element[0].selectedIndex=t.index,this.buttonItem.replaceWith(this.buttonItem=this._renderButtonItem(t)),this._setAria(t),this._trigger("select",e,{item:t}),t.index!==i&&this._trigger("change",e,{item:t}),this.close(e)},_setAria:function(t){t=this.menuItems.eq(t.index).attr("id");this.button.attr({"aria-labelledby":t,"aria-activedescendant":t}),this.menu.attr("aria-activedescendant",t)},_setOption:function(t,e){var i;"icons"===t&&(i=this.button.find("span.ui-icon"),this._removeClass(i,null,this.options.icons.button)._addClass(i,null,e.button)),this._super(t,e),"appendTo"===t&&this.menuWrap.appendTo(this._appendTo()),"width"===t&&this._resizeButton()},_setOptionDisabled:function(t){this._super(t),this.menuInstance.option("disabled",t),this.button.attr("aria-disabled",t),this._toggleClass(this.button,null,"ui-state-disabled",t),this.element.prop("disabled",t),t?(this.button.attr("tabindex",-1),this.close()):this.button.attr("tabindex",0)},_appendTo:function(){var t=this.options.appendTo;return t=!(t=!(t=t&&(t.jquery||t.nodeType?V(t):this.document.find(t).eq(0)))||!t[0]?this.element.closest(".ui-front, dialog"):t).length?this.document[0].body:t},_toggleAttr:function(){this.button.attr("aria-expanded",this.isOpen),this._removeClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"closed":"open"))._addClass(this.button,"ui-selectmenu-button-"+(this.isOpen?"open":"closed"))._toggleClass(this.menuWrap,"ui-selectmenu-open",null,this.isOpen),this.menu.attr("aria-hidden",!this.isOpen)},_resizeButton:function(){var t=this.options.width;!1!==t?(null===t&&(t=this.element.show().outerWidth(),this.element.hide()),this.button.outerWidth(t)):this.button.css("width","")},_resizeMenu:function(){this.menu.outerWidth(Math.max(this.button.outerWidth(),this.menu.width("").outerWidth()+1))},_getCreateOptions:function(){var t=this._super();return t.disabled=this.element.prop("disabled"),t},_parseOptions:function(t){var i=this,s=[];t.each(function(t,e){e.hidden||s.push(i._parseOption(V(e),t))}),this.items=s},_parseOption:function(t,e){var i=t.parent("optgroup");return{element:t,index:e,value:t.val(),label:t.text(),optgroup:i.attr("label")||"",disabled:i.prop("disabled")||t.prop("disabled")}},_destroy:function(){this._unbindFormResetHandler(),this.menuWrap.remove(),this.button.remove(),this.element.show(),this.element.removeUniqueId(),this.labels.attr("for",this.ids.element)}}]),V.widget("ui.slider",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"slide",options:{animate:!1,classes:{"ui-slider":"ui-corner-all","ui-slider-handle":"ui-corner-all","ui-slider-range":"ui-corner-all ui-widget-header"},distance:0,max:100,min:0,orientation:"horizontal",range:!1,step:1,value:0,values:null,change:null,slide:null,start:null,stop:null},numPages:5,_create:function(){this._keySliding=!1,this._mouseSliding=!1,this._animateOff=!0,this._handleIndex=null,this._detectOrientation(),this._mouseInit(),this._calculateNewMax(),this._addClass("ui-slider ui-slider-"+this.orientation,"ui-widget ui-widget-content"),this._refresh(),this._animateOff=!1},_refresh:function(){this._createRange(),this._createHandles(),this._setupEvents(),this._refreshValue()},_createHandles:function(){var t,e=this.options,i=this.element.find(".ui-slider-handle"),s=[],n=e.values&&e.values.length||1;for(i.length>n&&(i.slice(n).remove(),i=i.slice(0,n)),t=i.length;t<n;t++)s.push("<span tabindex='0'></span>");this.handles=i.add(V(s.join("")).appendTo(this.element)),this._addClass(this.handles,"ui-slider-handle","ui-state-default"),this.handle=this.handles.eq(0),this.handles.each(function(t){V(this).data("ui-slider-handle-index",t).attr("tabIndex",0)})},_createRange:function(){var t=this.options;t.range?(!0===t.range&&(t.values?t.values.length&&2!==t.values.length?t.values=[t.values[0],t.values[0]]:Array.isArray(t.values)&&(t.values=t.values.slice(0)):t.values=[this._valueMin(),this._valueMin()]),this.range&&this.range.length?(this._removeClass(this.range,"ui-slider-range-min ui-slider-range-max"),this.range.css({left:"",bottom:""})):(this.range=V("<div>").appendTo(this.element),this._addClass(this.range,"ui-slider-range")),"min"!==t.range&&"max"!==t.range||this._addClass(this.range,"ui-slider-range-"+t.range)):(this.range&&this.range.remove(),this.range=null)},_setupEvents:function(){this._off(this.handles),this._on(this.handles,this._handleEvents),this._hoverable(this.handles),this._focusable(this.handles)},_destroy:function(){this.handles.remove(),this.range&&this.range.remove(),this._mouseDestroy()},_mouseCapture:function(t){var i,s,n,o,e,a,r=this,l=this.options;return!l.disabled&&(this.elementSize={width:this.element.outerWidth(),height:this.element.outerHeight()},this.elementOffset=this.element.offset(),a={x:t.pageX,y:t.pageY},i=this._normValueFromMouse(a),s=this._valueMax()-this._valueMin()+1,this.handles.each(function(t){var e=Math.abs(i-r.values(t));(e<s||s===e&&(t===r._lastChangedValue||r.values(t)===l.min))&&(s=e,n=V(this),o=t)}),!1!==this._start(t,o)&&(this._mouseSliding=!0,this._handleIndex=o,this._addClass(n,null,"ui-state-active"),n.trigger("focus"),e=n.offset(),a=!V(t.target).parents().addBack().is(".ui-slider-handle"),this._clickOffset=a?{left:0,top:0}:{left:t.pageX-e.left-n.width()/2,top:t.pageY-e.top-n.height()/2-(parseInt(n.css("borderTopWidth"),10)||0)-(parseInt(n.css("borderBottomWidth"),10)||0)+(parseInt(n.css("marginTop"),10)||0)},this.handles.hasClass("ui-state-hover")||this._slide(t,o,i),this._animateOff=!0))},_mouseStart:function(){return!0},_mouseDrag:function(t){var e={x:t.pageX,y:t.pageY},e=this._normValueFromMouse(e);return this._slide(t,this._handleIndex,e),!1},_mouseStop:function(t){return this._removeClass(this.handles,null,"ui-state-active"),this._mouseSliding=!1,this._stop(t,this._handleIndex),this._change(t,this._handleIndex),this._handleIndex=null,this._clickOffset=null,this._animateOff=!1},_detectOrientation:function(){this.orientation="vertical"===this.options.orientation?"vertical":"horizontal"},_normValueFromMouse:function(t){var e,t="horizontal"===this.orientation?(e=this.elementSize.width,t.x-this.elementOffset.left-(this._clickOffset?this._clickOffset.left:0)):(e=this.elementSize.height,t.y-this.elementOffset.top-(this._clickOffset?this._clickOffset.top:0)),t=t/e;return(t=1<t?1:t)<0&&(t=0),"vertical"===this.orientation&&(t=1-t),e=this._valueMax()-this._valueMin(),e=this._valueMin()+t*e,this._trimAlignValue(e)},_uiHash:function(t,e,i){var s={handle:this.handles[t],handleIndex:t,value:void 0!==e?e:this.value()};return this._hasMultipleValues()&&(s.value=void 0!==e?e:this.values(t),s.values=i||this.values()),s},_hasMultipleValues:function(){return this.options.values&&this.options.values.length},_start:function(t,e){return this._trigger("start",t,this._uiHash(e))},_slide:function(t,e,i){var s,n=this.value(),o=this.values();this._hasMultipleValues()&&(s=this.values(e?0:1),n=this.values(e),2===this.options.values.length&&!0===this.options.range&&(i=0===e?Math.min(s,i):Math.max(s,i)),o[e]=i),i!==n&&!1!==this._trigger("slide",t,this._uiHash(e,i,o))&&(this._hasMultipleValues()?this.values(e,i):this.value(i))},_stop:function(t,e){this._trigger("stop",t,this._uiHash(e))},_change:function(t,e){this._keySliding||this._mouseSliding||(this._lastChangedValue=e,this._trigger("change",t,this._uiHash(e)))},value:function(t){return arguments.length?(this.options.value=this._trimAlignValue(t),this._refreshValue(),void this._change(null,0)):this._value()},values:function(t,e){var i,s,n;if(1<arguments.length)return this.options.values[t]=this._trimAlignValue(e),this._refreshValue(),void this._change(null,t);if(!arguments.length)return this._values();if(!Array.isArray(t))return this._hasMultipleValues()?this._values(t):this.value();for(i=this.options.values,s=t,n=0;n<i.length;n+=1)i[n]=this._trimAlignValue(s[n]),this._change(null,n);this._refreshValue()},_setOption:function(t,e){var i,s=0;switch("range"===t&&!0===this.options.range&&("min"===e?(this.options.value=this._values(0),this.options.values=null):"max"===e&&(this.options.value=this._values(this.options.values.length-1),this.options.values=null)),Array.isArray(this.options.values)&&(s=this.options.values.length),this._super(t,e),t){case"orientation":this._detectOrientation(),this._removeClass("ui-slider-horizontal ui-slider-vertical")._addClass("ui-slider-"+this.orientation),this._refreshValue(),this.options.range&&this._refreshRange(e),this.handles.css("horizontal"===e?"bottom":"left","");break;case"value":this._animateOff=!0,this._refreshValue(),this._change(null,0),this._animateOff=!1;break;case"values":for(this._animateOff=!0,this._refreshValue(),i=s-1;0<=i;i--)this._change(null,i);this._animateOff=!1;break;case"step":case"min":case"max":this._animateOff=!0,this._calculateNewMax(),this._refreshValue(),this._animateOff=!1;break;case"range":this._animateOff=!0,this._refresh(),this._animateOff=!1}},_setOptionDisabled:function(t){this._super(t),this._toggleClass(null,"ui-state-disabled",!!t)},_value:function(){var t=this.options.value;return t=this._trimAlignValue(t)},_values:function(t){var e,i;if(arguments.length)return t=this.options.values[t],t=this._trimAlignValue(t);if(this._hasMultipleValues()){for(e=this.options.values.slice(),i=0;i<e.length;i+=1)e[i]=this._trimAlignValue(e[i]);return e}return[]},_trimAlignValue:function(t){if(t<=this._valueMin())return this._valueMin();if(t>=this._valueMax())return this._valueMax();var e=0<this.options.step?this.options.step:1,i=(t-this._valueMin())%e,t=t-i;return 2*Math.abs(i)>=e&&(t+=0<i?e:-e),parseFloat(t.toFixed(5))},_calculateNewMax:function(){var t=this.options.max,e=this._valueMin(),i=this.options.step;(t=Math.round((t-e)/i)*i+e)>this.options.max&&(t-=i),this.max=parseFloat(t.toFixed(this._precision()))},_precision:function(){var t=this._precisionOf(this.options.step);return t=null!==this.options.min?Math.max(t,this._precisionOf(this.options.min)):t},_precisionOf:function(t){var e=t.toString(),t=e.indexOf(".");return-1===t?0:e.length-t-1},_valueMin:function(){return this.options.min},_valueMax:function(){return this.max},_refreshRange:function(t){"vertical"===t&&this.range.css({width:"",left:""}),"horizontal"===t&&this.range.css({height:"",bottom:""})},_refreshValue:function(){var e,i,t,s,n,o=this.options.range,a=this.options,r=this,l=!this._animateOff&&a.animate,h={};this._hasMultipleValues()?this.handles.each(function(t){i=(r.values(t)-r._valueMin())/(r._valueMax()-r._valueMin())*100,h["horizontal"===r.orientation?"left":"bottom"]=i+"%",V(this).stop(1,1)[l?"animate":"css"](h,a.animate),!0===r.options.range&&("horizontal"===r.orientation?(0===t&&r.range.stop(1,1)[l?"animate":"css"]({left:i+"%"},a.animate),1===t&&r.range[l?"animate":"css"]({width:i-e+"%"},{queue:!1,duration:a.animate})):(0===t&&r.range.stop(1,1)[l?"animate":"css"]({bottom:i+"%"},a.animate),1===t&&r.range[l?"animate":"css"]({height:i-e+"%"},{queue:!1,duration:a.animate}))),e=i}):(t=this.value(),s=this._valueMin(),n=this._valueMax(),i=n!==s?(t-s)/(n-s)*100:0,h["horizontal"===this.orientation?"left":"bottom"]=i+"%",this.handle.stop(1,1)[l?"animate":"css"](h,a.animate),"min"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:i+"%"},a.animate),"max"===o&&"horizontal"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({width:100-i+"%"},a.animate),"min"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:i+"%"},a.animate),"max"===o&&"vertical"===this.orientation&&this.range.stop(1,1)[l?"animate":"css"]({height:100-i+"%"},a.animate))},_handleEvents:{keydown:function(t){var e,i,s,n=V(t.target).data("ui-slider-handle-index");switch(t.keyCode){case V.ui.keyCode.HOME:case V.ui.keyCode.END:case V.ui.keyCode.PAGE_UP:case V.ui.keyCode.PAGE_DOWN:case V.ui.keyCode.UP:case V.ui.keyCode.RIGHT:case V.ui.keyCode.DOWN:case V.ui.keyCode.LEFT:if(t.preventDefault(),!this._keySliding&&(this._keySliding=!0,this._addClass(V(t.target),null,"ui-state-active"),!1===this._start(t,n)))return}switch(s=this.options.step,e=i=this._hasMultipleValues()?this.values(n):this.value(),t.keyCode){case V.ui.keyCode.HOME:i=this._valueMin();break;case V.ui.keyCode.END:i=this._valueMax();break;case V.ui.keyCode.PAGE_UP:i=this._trimAlignValue(e+(this._valueMax()-this._valueMin())/this.numPages);break;case V.ui.keyCode.PAGE_DOWN:i=this._trimAlignValue(e-(this._valueMax()-this._valueMin())/this.numPages);break;case V.ui.keyCode.UP:case V.ui.keyCode.RIGHT:if(e===this._valueMax())return;i=this._trimAlignValue(e+s);break;case V.ui.keyCode.DOWN:case V.ui.keyCode.LEFT:if(e===this._valueMin())return;i=this._trimAlignValue(e-s)}this._slide(t,n,i)},keyup:function(t){var e=V(t.target).data("ui-slider-handle-index");this._keySliding&&(this._keySliding=!1,this._stop(t,e),this._change(t,e),this._removeClass(V(t.target),null,"ui-state-active"))}}}),V.widget("ui.sortable",V.ui.mouse,{version:"1.13.2",widgetEventPrefix:"sort",ready:!1,options:{appendTo:"parent",axis:!1,connectWith:!1,containment:!1,cursor:"auto",cursorAt:!1,dropOnEmpty:!0,forcePlaceholderSize:!1,forceHelperSize:!1,grid:!1,handle:!1,helper:"original",items:"> *",opacity:!1,placeholder:!1,revert:!1,scroll:!0,scrollSensitivity:20,scrollSpeed:20,scope:"default",tolerance:"intersect",zIndex:1e3,activate:null,beforeStop:null,change:null,deactivate:null,out:null,over:null,receive:null,remove:null,sort:null,start:null,stop:null,update:null},_isOverAxis:function(t,e,i){return e<=t&&t<e+i},_isFloating:function(t){return/left|right/.test(t.css("float"))||/inline|table-cell/.test(t.css("display"))},_create:function(){this.containerCache={},this._addClass("ui-sortable"),this.refresh(),this.offset=this.element.offset(),this._mouseInit(),this._setHandleClassName(),this.ready=!0},_setOption:function(t,e){this._super(t,e),"handle"===t&&this._setHandleClassName()},_setHandleClassName:function(){var t=this;this._removeClass(this.element.find(".ui-sortable-handle"),"ui-sortable-handle"),V.each(this.items,function(){t._addClass(this.instance.options.handle?this.item.find(this.instance.options.handle):this.item,"ui-sortable-handle")})},_destroy:function(){this._mouseDestroy();for(var t=this.items.length-1;0<=t;t--)this.items[t].item.removeData(this.widgetName+"-item");return this},_mouseCapture:function(t,e){var i=null,s=!1,n=this;return!this.reverting&&(!this.options.disabled&&"static"!==this.options.type&&(this._refreshItems(t),V(t.target).parents().each(function(){if(V.data(this,n.widgetName+"-item")===n)return i=V(this),!1}),!!(i=V.data(t.target,n.widgetName+"-item")===n?V(t.target):i)&&(!(this.options.handle&&!e&&(V(this.options.handle,i).find("*").addBack().each(function(){this===t.target&&(s=!0)}),!s))&&(this.currentItem=i,this._removeCurrentsFromItems(),!0))))},_mouseStart:function(t,e,i){var s,n,o=this.options;if((this.currentContainer=this).refreshPositions(),this.appendTo=V("parent"!==o.appendTo?o.appendTo:this.currentItem.parent()),this.helper=this._createHelper(t),this._cacheHelperProportions(),this._cacheMargins(),this.offset=this.currentItem.offset(),this.offset={top:this.offset.top-this.margins.top,left:this.offset.left-this.margins.left},V.extend(this.offset,{click:{left:t.pageX-this.offset.left,top:t.pageY-this.offset.top},relative:this._getRelativeOffset()}),this.helper.css("position","absolute"),this.cssPosition=this.helper.css("position"),o.cursorAt&&this._adjustOffsetFromHelper(o.cursorAt),this.domPosition={prev:this.currentItem.prev()[0],parent:this.currentItem.parent()[0]},this.helper[0]!==this.currentItem[0]&&this.currentItem.hide(),this._createPlaceholder(),this.scrollParent=this.placeholder.scrollParent(),V.extend(this.offset,{parent:this._getParentOffset()}),o.containment&&this._setContainment(),o.cursor&&"auto"!==o.cursor&&(n=this.document.find("body"),this.storedCursor=n.css("cursor"),n.css("cursor",o.cursor),this.storedStylesheet=V("<style>*{ cursor: "+o.cursor+" !important; }</style>").appendTo(n)),o.zIndex&&(this.helper.css("zIndex")&&(this._storedZIndex=this.helper.css("zIndex")),this.helper.css("zIndex",o.zIndex)),o.opacity&&(this.helper.css("opacity")&&(this._storedOpacity=this.helper.css("opacity")),this.helper.css("opacity",o.opacity)),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this._trigger("start",t,this._uiHash()),this._preserveHelperProportions||this._cacheHelperProportions(),!i)for(s=this.containers.length-1;0<=s;s--)this.containers[s]._trigger("activate",t,this._uiHash(this));return V.ui.ddmanager&&(V.ui.ddmanager.current=this),V.ui.ddmanager&&!o.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t),this.dragging=!0,this._addClass(this.helper,"ui-sortable-helper"),this.helper.parent().is(this.appendTo)||(this.helper.detach().appendTo(this.appendTo),this.offset.parent=this._getParentOffset()),this.position=this.originalPosition=this._generatePosition(t),this.originalPageX=t.pageX,this.originalPageY=t.pageY,this.lastPositionAbs=this.positionAbs=this._convertPositionTo("absolute"),this._mouseDrag(t),!0},_scroll:function(t){var e=this.options,i=!1;return this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName?(this.overflowOffset.top+this.scrollParent[0].offsetHeight-t.pageY<e.scrollSensitivity?this.scrollParent[0].scrollTop=i=this.scrollParent[0].scrollTop+e.scrollSpeed:t.pageY-this.overflowOffset.top<e.scrollSensitivity&&(this.scrollParent[0].scrollTop=i=this.scrollParent[0].scrollTop-e.scrollSpeed),this.overflowOffset.left+this.scrollParent[0].offsetWidth-t.pageX<e.scrollSensitivity?this.scrollParent[0].scrollLeft=i=this.scrollParent[0].scrollLeft+e.scrollSpeed:t.pageX-this.overflowOffset.left<e.scrollSensitivity&&(this.scrollParent[0].scrollLeft=i=this.scrollParent[0].scrollLeft-e.scrollSpeed)):(t.pageY-this.document.scrollTop()<e.scrollSensitivity?i=this.document.scrollTop(this.document.scrollTop()-e.scrollSpeed):this.window.height()-(t.pageY-this.document.scrollTop())<e.scrollSensitivity&&(i=this.document.scrollTop(this.document.scrollTop()+e.scrollSpeed)),t.pageX-this.document.scrollLeft()<e.scrollSensitivity?i=this.document.scrollLeft(this.document.scrollLeft()-e.scrollSpeed):this.window.width()-(t.pageX-this.document.scrollLeft())<e.scrollSensitivity&&(i=this.document.scrollLeft(this.document.scrollLeft()+e.scrollSpeed))),i},_mouseDrag:function(t){var e,i,s,n,o=this.options;for(this.position=this._generatePosition(t),this.positionAbs=this._convertPositionTo("absolute"),this.options.axis&&"y"===this.options.axis||(this.helper[0].style.left=this.position.left+"px"),this.options.axis&&"x"===this.options.axis||(this.helper[0].style.top=this.position.top+"px"),o.scroll&&!1!==this._scroll(t)&&(this._refreshItemPositions(!0),V.ui.ddmanager&&!o.dropBehaviour&&V.ui.ddmanager.prepareOffsets(this,t)),this.dragDirection={vertical:this._getDragVerticalDirection(),horizontal:this._getDragHorizontalDirection()},e=this.items.length-1;0<=e;e--)if(s=(i=this.items[e]).item[0],(n=this._intersectsWithPointer(i))&&i.instance===this.currentContainer&&!(s===this.currentItem[0]||this.placeholder[1===n?"next":"prev"]()[0]===s||V.contains(this.placeholder[0],s)||"semi-dynamic"===this.options.type&&V.contains(this.element[0],s))){if(this.direction=1===n?"down":"up","pointer"!==this.options.tolerance&&!this._intersectsWithSides(i))break;this._rearrange(t,i),this._trigger("change",t,this._uiHash());break}return this._contactContainers(t),V.ui.ddmanager&&V.ui.ddmanager.drag(this,t),this._trigger("sort",t,this._uiHash()),this.lastPositionAbs=this.positionAbs,!1},_mouseStop:function(t,e){var i,s,n,o;if(t)return V.ui.ddmanager&&!this.options.dropBehaviour&&V.ui.ddmanager.drop(this,t),this.options.revert?(s=(i=this).placeholder.offset(),o={},(n=this.options.axis)&&"x"!==n||(o.left=s.left-this.offset.parent.left-this.margins.left+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollLeft)),n&&"y"!==n||(o.top=s.top-this.offset.parent.top-this.margins.top+(this.offsetParent[0]===this.document[0].body?0:this.offsetParent[0].scrollTop)),this.reverting=!0,V(this.helper).animate(o,parseInt(this.options.revert,10)||500,function(){i._clear(t)})):this._clear(t,e),!1},cancel:function(){if(this.dragging){this._mouseUp(new V.Event("mouseup",{target:null})),"original"===this.options.helper?(this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")):this.currentItem.show();for(var t=this.containers.length-1;0<=t;t--)this.containers[t]._trigger("deactivate",null,this._uiHash(this)),this.containers[t].containerCache.over&&(this.containers[t]._trigger("out",null,this._uiHash(this)),this.containers[t].containerCache.over=0)}return this.placeholder&&(this.placeholder[0].parentNode&&this.placeholder[0].parentNode.removeChild(this.placeholder[0]),"original"!==this.options.helper&&this.helper&&this.helper[0].parentNode&&this.helper.remove(),V.extend(this,{helper:null,dragging:!1,reverting:!1,_noFinalSort:null}),this.domPosition.prev?V(this.domPosition.prev).after(this.currentItem):V(this.domPosition.parent).prepend(this.currentItem)),this},serialize:function(e){var t=this._getItemsAsjQuery(e&&e.connected),i=[];return e=e||{},V(t).each(function(){var t=(V(e.item||this).attr(e.attribute||"id")||"").match(e.expression||/(.+)[\-=_](.+)/);t&&i.push((e.key||t[1]+"[]")+"="+(e.key&&e.expression?t[1]:t[2]))}),!i.length&&e.key&&i.push(e.key+"="),i.join("&")},toArray:function(t){var e=this._getItemsAsjQuery(t&&t.connected),i=[];return t=t||{},e.each(function(){i.push(V(t.item||this).attr(t.attribute||"id")||"")}),i},_intersectsWith:function(t){var e=this.positionAbs.left,i=e+this.helperProportions.width,s=this.positionAbs.top,n=s+this.helperProportions.height,o=t.left,a=o+t.width,r=t.top,l=r+t.height,h=this.offset.click.top,c=this.offset.click.left,h="x"===this.options.axis||r<s+h&&s+h<l,c="y"===this.options.axis||o<e+c&&e+c<a;return"pointer"===this.options.tolerance||this.options.forcePointerForContainers||"pointer"!==this.options.tolerance&&this.helperProportions[this.floating?"width":"height"]>t[this.floating?"width":"height"]?h&&c:o<e+this.helperProportions.width/2&&i-this.helperProportions.width/2<a&&r<s+this.helperProportions.height/2&&n-this.helperProportions.height/2<l},_intersectsWithPointer:function(t){var e="x"===this.options.axis||this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top,t.height),t="y"===this.options.axis||this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left,t.width);return!(!e||!t)&&(e=this.dragDirection.vertical,t=this.dragDirection.horizontal,this.floating?"right"===t||"down"===e?2:1:e&&("down"===e?2:1))},_intersectsWithSides:function(t){var e=this._isOverAxis(this.positionAbs.top+this.offset.click.top,t.top+t.height/2,t.height),i=this._isOverAxis(this.positionAbs.left+this.offset.click.left,t.left+t.width/2,t.width),s=this.dragDirection.vertical,t=this.dragDirection.horizontal;return this.floating&&t?"right"===t&&i||"left"===t&&!i:s&&("down"===s&&e||"up"===s&&!e)},_getDragVerticalDirection:function(){var t=this.positionAbs.top-this.lastPositionAbs.top;return 0!=t&&(0<t?"down":"up")},_getDragHorizontalDirection:function(){var t=this.positionAbs.left-this.lastPositionAbs.left;return 0!=t&&(0<t?"right":"left")},refresh:function(t){return this._refreshItems(t),this._setHandleClassName(),this.refreshPositions(),this},_connectWith:function(){var t=this.options;return t.connectWith.constructor===String?[t.connectWith]:t.connectWith},_getItemsAsjQuery:function(t){var e,i,s,n,o=[],a=[],r=this._connectWith();if(r&&t)for(e=r.length-1;0<=e;e--)for(i=(s=V(r[e],this.document[0])).length-1;0<=i;i--)(n=V.data(s[i],this.widgetFullName))&&n!==this&&!n.options.disabled&&a.push(["function"==typeof n.options.items?n.options.items.call(n.element):V(n.options.items,n.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),n]);function l(){o.push(this)}for(a.push(["function"==typeof this.options.items?this.options.items.call(this.element,null,{options:this.options,item:this.currentItem}):V(this.options.items,this.element).not(".ui-sortable-helper").not(".ui-sortable-placeholder"),this]),e=a.length-1;0<=e;e--)a[e][0].each(l);return V(o)},_removeCurrentsFromItems:function(){var i=this.currentItem.find(":data("+this.widgetName+"-item)");this.items=V.grep(this.items,function(t){for(var e=0;e<i.length;e++)if(i[e]===t.item[0])return!1;return!0})},_refreshItems:function(t){this.items=[],this.containers=[this];var e,i,s,n,o,a,r,l,h=this.items,c=[["function"==typeof this.options.items?this.options.items.call(this.element[0],t,{item:this.currentItem}):V(this.options.items,this.element),this]],u=this._connectWith();if(u&&this.ready)for(e=u.length-1;0<=e;e--)for(i=(s=V(u[e],this.document[0])).length-1;0<=i;i--)(n=V.data(s[i],this.widgetFullName))&&n!==this&&!n.options.disabled&&(c.push(["function"==typeof n.options.items?n.options.items.call(n.element[0],t,{item:this.currentItem}):V(n.options.items,n.element),n]),this.containers.push(n));for(e=c.length-1;0<=e;e--)for(o=c[e][1],l=(a=c[e][i=0]).length;i<l;i++)(r=V(a[i])).data(this.widgetName+"-item",o),h.push({item:r,instance:o,width:0,height:0,left:0,top:0})},_refreshItemPositions:function(t){for(var e,i,s=this.items.length-1;0<=s;s--)e=this.items[s],this.currentContainer&&e.instance!==this.currentContainer&&e.item[0]!==this.currentItem[0]||(i=this.options.toleranceElement?V(this.options.toleranceElement,e.item):e.item,t||(e.width=i.outerWidth(),e.height=i.outerHeight()),i=i.offset(),e.left=i.left,e.top=i.top)},refreshPositions:function(t){var e,i;if(this.floating=!!this.items.length&&("x"===this.options.axis||this._isFloating(this.items[0].item)),this.offsetParent&&this.helper&&(this.offset.parent=this._getParentOffset()),this._refreshItemPositions(t),this.options.custom&&this.options.custom.refreshContainers)this.options.custom.refreshContainers.call(this);else for(e=this.containers.length-1;0<=e;e--)i=this.containers[e].element.offset(),this.containers[e].containerCache.left=i.left,this.containers[e].containerCache.top=i.top,this.containers[e].containerCache.width=this.containers[e].element.outerWidth(),this.containers[e].containerCache.height=this.containers[e].element.outerHeight();return this},_createPlaceholder:function(i){var s,n,o=(i=i||this).options;o.placeholder&&o.placeholder.constructor!==String||(s=o.placeholder,n=i.currentItem[0].nodeName.toLowerCase(),o.placeholder={element:function(){var t=V("<"+n+">",i.document[0]);return i._addClass(t,"ui-sortable-placeholder",s||i.currentItem[0].className)._removeClass(t,"ui-sortable-helper"),"tbody"===n?i._createTrPlaceholder(i.currentItem.find("tr").eq(0),V("<tr>",i.document[0]).appendTo(t)):"tr"===n?i._createTrPlaceholder(i.currentItem,t):"img"===n&&t.attr("src",i.currentItem.attr("src")),s||t.css("visibility","hidden"),t},update:function(t,e){s&&!o.forcePlaceholderSize||(e.height()&&(!o.forcePlaceholderSize||"tbody"!==n&&"tr"!==n)||e.height(i.currentItem.innerHeight()-parseInt(i.currentItem.css("paddingTop")||0,10)-parseInt(i.currentItem.css("paddingBottom")||0,10)),e.width()||e.width(i.currentItem.innerWidth()-parseInt(i.currentItem.css("paddingLeft")||0,10)-parseInt(i.currentItem.css("paddingRight")||0,10)))}}),i.placeholder=V(o.placeholder.element.call(i.element,i.currentItem)),i.currentItem.after(i.placeholder),o.placeholder.update(i,i.placeholder)},_createTrPlaceholder:function(t,e){var i=this;t.children().each(function(){V("<td>&#160;</td>",i.document[0]).attr("colspan",V(this).attr("colspan")||1).appendTo(e)})},_contactContainers:function(t){for(var e,i,s,n,o,a,r,l,h,c=null,u=null,d=this.containers.length-1;0<=d;d--)V.contains(this.currentItem[0],this.containers[d].element[0])||(this._intersectsWith(this.containers[d].containerCache)?c&&V.contains(this.containers[d].element[0],c.element[0])||(c=this.containers[d],u=d):this.containers[d].containerCache.over&&(this.containers[d]._trigger("out",t,this._uiHash(this)),this.containers[d].containerCache.over=0));if(c)if(1===this.containers.length)this.containers[u].containerCache.over||(this.containers[u]._trigger("over",t,this._uiHash(this)),this.containers[u].containerCache.over=1);else{for(i=1e4,s=null,n=(l=c.floating||this._isFloating(this.currentItem))?"left":"top",o=l?"width":"height",h=l?"pageX":"pageY",e=this.items.length-1;0<=e;e--)V.contains(this.containers[u].element[0],this.items[e].item[0])&&this.items[e].item[0]!==this.currentItem[0]&&(a=this.items[e].item.offset()[n],r=!1,t[h]-a>this.items[e][o]/2&&(r=!0),Math.abs(t[h]-a)<i&&(i=Math.abs(t[h]-a),s=this.items[e],this.direction=r?"up":"down"));(s||this.options.dropOnEmpty)&&(this.currentContainer!==this.containers[u]?(s?this._rearrange(t,s,null,!0):this._rearrange(t,null,this.containers[u].element,!0),this._trigger("change",t,this._uiHash()),this.containers[u]._trigger("change",t,this._uiHash(this)),this.currentContainer=this.containers[u],this.options.placeholder.update(this.currentContainer,this.placeholder),this.scrollParent=this.placeholder.scrollParent(),this.scrollParent[0]!==this.document[0]&&"HTML"!==this.scrollParent[0].tagName&&(this.overflowOffset=this.scrollParent.offset()),this.containers[u]._trigger("over",t,this._uiHash(this)),this.containers[u].containerCache.over=1):this.currentContainer.containerCache.over||(this.containers[u]._trigger("over",t,this._uiHash()),this.currentContainer.containerCache.over=1))}},_createHelper:function(t){var e=this.options,t="function"==typeof e.helper?V(e.helper.apply(this.element[0],[t,this.currentItem])):"clone"===e.helper?this.currentItem.clone():this.currentItem;return t.parents("body").length||this.appendTo[0].appendChild(t[0]),t[0]===this.currentItem[0]&&(this._storedCSS={width:this.currentItem[0].style.width,height:this.currentItem[0].style.height,position:this.currentItem.css("position"),top:this.currentItem.css("top"),left:this.currentItem.css("left")}),t[0].style.width&&!e.forceHelperSize||t.width(this.currentItem.width()),t[0].style.height&&!e.forceHelperSize||t.height(this.currentItem.height()),t},_adjustOffsetFromHelper:function(t){"string"==typeof t&&(t=t.split(" ")),"left"in(t=Array.isArray(t)?{left:+t[0],top:+t[1]||0}:t)&&(this.offset.click.left=t.left+this.margins.left),"right"in t&&(this.offset.click.left=this.helperProportions.width-t.right+this.margins.left),"top"in t&&(this.offset.click.top=t.top+this.margins.top),"bottom"in t&&(this.offset.click.top=this.helperProportions.height-t.bottom+this.margins.top)},_getParentOffset:function(){this.offsetParent=this.helper.offsetParent();var t=this.offsetParent.offset();return"absolute"===this.cssPosition&&this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])&&(t.left+=this.scrollParent.scrollLeft(),t.top+=this.scrollParent.scrollTop()),{top:(t=this.offsetParent[0]===this.document[0].body||this.offsetParent[0].tagName&&"html"===this.offsetParent[0].tagName.toLowerCase()&&V.ui.ie?{top:0,left:0}:t).top+(parseInt(this.offsetParent.css("borderTopWidth"),10)||0),left:t.left+(parseInt(this.offsetParent.css("borderLeftWidth"),10)||0)}},_getRelativeOffset:function(){if("relative"!==this.cssPosition)return{top:0,left:0};var t=this.currentItem.position();return{top:t.top-(parseInt(this.helper.css("top"),10)||0)+this.scrollParent.scrollTop(),left:t.left-(parseInt(this.helper.css("left"),10)||0)+this.scrollParent.scrollLeft()}},_cacheMargins:function(){this.margins={left:parseInt(this.currentItem.css("marginLeft"),10)||0,top:parseInt(this.currentItem.css("marginTop"),10)||0}},_cacheHelperProportions:function(){this.helperProportions={width:this.helper.outerWidth(),height:this.helper.outerHeight()}},_setContainment:function(){var t,e,i=this.options;"parent"===i.containment&&(i.containment=this.helper[0].parentNode),"document"!==i.containment&&"window"!==i.containment||(this.containment=[0-this.offset.relative.left-this.offset.parent.left,0-this.offset.relative.top-this.offset.parent.top,"document"===i.containment?this.document.width():this.window.width()-this.helperProportions.width-this.margins.left,("document"===i.containment?this.document.height()||document.body.parentNode.scrollHeight:this.window.height()||this.document[0].body.parentNode.scrollHeight)-this.helperProportions.height-this.margins.top]),/^(document|window|parent)$/.test(i.containment)||(t=V(i.containment)[0],e=V(i.containment).offset(),i="hidden"!==V(t).css("overflow"),this.containment=[e.left+(parseInt(V(t).css("borderLeftWidth"),10)||0)+(parseInt(V(t).css("paddingLeft"),10)||0)-this.margins.left,e.top+(parseInt(V(t).css("borderTopWidth"),10)||0)+(parseInt(V(t).css("paddingTop"),10)||0)-this.margins.top,e.left+(i?Math.max(t.scrollWidth,t.offsetWidth):t.offsetWidth)-(parseInt(V(t).css("borderLeftWidth"),10)||0)-(parseInt(V(t).css("paddingRight"),10)||0)-this.helperProportions.width-this.margins.left,e.top+(i?Math.max(t.scrollHeight,t.offsetHeight):t.offsetHeight)-(parseInt(V(t).css("borderTopWidth"),10)||0)-(parseInt(V(t).css("paddingBottom"),10)||0)-this.helperProportions.height-this.margins.top])},_convertPositionTo:function(t,e){e=e||this.position;var i="absolute"===t?1:-1,s="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,t=/(html|body)/i.test(s[0].tagName);return{top:e.top+this.offset.relative.top*i+this.offset.parent.top*i-("fixed"===this.cssPosition?-this.scrollParent.scrollTop():t?0:s.scrollTop())*i,left:e.left+this.offset.relative.left*i+this.offset.parent.left*i-("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():t?0:s.scrollLeft())*i}},_generatePosition:function(t){var e=this.options,i=t.pageX,s=t.pageY,n="absolute"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&V.contains(this.scrollParent[0],this.offsetParent[0])?this.scrollParent:this.offsetParent,o=/(html|body)/i.test(n[0].tagName);return"relative"!==this.cssPosition||this.scrollParent[0]!==this.document[0]&&this.scrollParent[0]!==this.offsetParent[0]||(this.offset.relative=this._getRelativeOffset()),this.originalPosition&&(this.containment&&(t.pageX-this.offset.click.left<this.containment[0]&&(i=this.containment[0]+this.offset.click.left),t.pageY-this.offset.click.top<this.containment[1]&&(s=this.containment[1]+this.offset.click.top),t.pageX-this.offset.click.left>this.containment[2]&&(i=this.containment[2]+this.offset.click.left),t.pageY-this.offset.click.top>this.containment[3]&&(s=this.containment[3]+this.offset.click.top)),e.grid&&(t=this.originalPageY+Math.round((s-this.originalPageY)/e.grid[1])*e.grid[1],s=!this.containment||t-this.offset.click.top>=this.containment[1]&&t-this.offset.click.top<=this.containment[3]?t:t-this.offset.click.top>=this.containment[1]?t-e.grid[1]:t+e.grid[1],t=this.originalPageX+Math.round((i-this.originalPageX)/e.grid[0])*e.grid[0],i=!this.containment||t-this.offset.click.left>=this.containment[0]&&t-this.offset.click.left<=this.containment[2]?t:t-this.offset.click.left>=this.containment[0]?t-e.grid[0]:t+e.grid[0])),{top:s-this.offset.click.top-this.offset.relative.top-this.offset.parent.top+("fixed"===this.cssPosition?-this.scrollParent.scrollTop():o?0:n.scrollTop()),left:i-this.offset.click.left-this.offset.relative.left-this.offset.parent.left+("fixed"===this.cssPosition?-this.scrollParent.scrollLeft():o?0:n.scrollLeft())}},_rearrange:function(t,e,i,s){i?i[0].appendChild(this.placeholder[0]):e.item[0].parentNode.insertBefore(this.placeholder[0],"down"===this.direction?e.item[0]:e.item[0].nextSibling),this.counter=this.counter?++this.counter:1;var n=this.counter;this._delay(function(){n===this.counter&&this.refreshPositions(!s)})},_clear:function(t,e){this.reverting=!1;var i,s=[];if(!this._noFinalSort&&this.currentItem.parent().length&&this.placeholder.before(this.currentItem),this._noFinalSort=null,this.helper[0]===this.currentItem[0]){for(i in this._storedCSS)"auto"!==this._storedCSS[i]&&"static"!==this._storedCSS[i]||(this._storedCSS[i]="");this.currentItem.css(this._storedCSS),this._removeClass(this.currentItem,"ui-sortable-helper")}else this.currentItem.show();function n(e,i,s){return function(t){s._trigger(e,t,i._uiHash(i))}}for(this.fromOutside&&!e&&s.push(function(t){this._trigger("receive",t,this._uiHash(this.fromOutside))}),!this.fromOutside&&this.domPosition.prev===this.currentItem.prev().not(".ui-sortable-helper")[0]&&this.domPosition.parent===this.currentItem.parent()[0]||e||s.push(function(t){this._trigger("update",t,this._uiHash())}),this!==this.currentContainer&&(e||(s.push(function(t){this._trigger("remove",t,this._uiHash())}),s.push(function(e){return function(t){e._trigger("receive",t,this._uiHash(this))}}.call(this,this.currentContainer)),s.push(function(e){return function(t){e._trigger("update",t,this._uiHash(this))}}.call(this,this.currentContainer)))),i=this.containers.length-1;0<=i;i--)e||s.push(n("deactivate",this,this.containers[i])),this.containers[i].containerCache.over&&(s.push(n("out",this,this.containers[i])),this.containers[i].containerCache.over=0);if(this.storedCursor&&(this.document.find("body").css("cursor",this.storedCursor),this.storedStylesheet.remove()),this._storedOpacity&&this.helper.css("opacity",this._storedOpacity),this._storedZIndex&&this.helper.css("zIndex","auto"===this._storedZIndex?"":this._storedZIndex),this.dragging=!1,e||this._trigger("beforeStop",t,this._uiHash()),this.placeholder[0].parentNode.removeChild(this.placeholder[0]),this.cancelHelperRemoval||(this.helper[0]!==this.currentItem[0]&&this.helper.remove(),this.helper=null),!e){for(i=0;i<s.length;i++)s[i].call(this,t);this._trigger("stop",t,this._uiHash())}return this.fromOutside=!1,!this.cancelHelperRemoval},_trigger:function(){!1===V.Widget.prototype._trigger.apply(this,arguments)&&this.cancel()},_uiHash:function(t){var e=t||this;return{helper:e.helper,placeholder:e.placeholder||V([]),position:e.position,originalPosition:e.originalPosition,offset:e.positionAbs,item:e.currentItem,sender:t?t.element:null}}});function ht(e){return function(){var t=this.element.val();e.apply(this,arguments),this._refresh(),t!==this.element.val()&&this._trigger("change")}}V.widget("ui.spinner",{version:"1.13.2",defaultElement:"<input>",widgetEventPrefix:"spin",options:{classes:{"ui-spinner":"ui-corner-all","ui-spinner-down":"ui-corner-br","ui-spinner-up":"ui-corner-tr"},culture:null,icons:{down:"ui-icon-triangle-1-s",up:"ui-icon-triangle-1-n"},incremental:!0,max:null,min:null,numberFormat:null,page:10,step:1,change:null,spin:null,start:null,stop:null},_create:function(){this._setOption("max",this.options.max),this._setOption("min",this.options.min),this._setOption("step",this.options.step),""!==this.value()&&this._value(this.element.val(),!0),this._draw(),this._on(this._events),this._refresh(),this._on(this.window,{beforeunload:function(){this.element.removeAttr("autocomplete")}})},_getCreateOptions:function(){var s=this._super(),n=this.element;return V.each(["min","max","step"],function(t,e){var i=n.attr(e);null!=i&&i.length&&(s[e]=i)}),s},_events:{keydown:function(t){this._start(t)&&this._keydown(t)&&t.preventDefault()},keyup:"_stop",focus:function(){this.previous=this.element.val()},blur:function(t){this.cancelBlur?delete this.cancelBlur:(this._stop(),this._refresh(),this.previous!==this.element.val()&&this._trigger("change",t))},mousewheel:function(t,e){var i=V.ui.safeActiveElement(this.document[0]);if(this.element[0]===i&&e){if(!this.spinning&&!this._start(t))return!1;this._spin((0<e?1:-1)*this.options.step,t),clearTimeout(this.mousewheelTimer),this.mousewheelTimer=this._delay(function(){this.spinning&&this._stop(t)},100),t.preventDefault()}},"mousedown .ui-spinner-button":function(t){var e;function i(){this.element[0]===V.ui.safeActiveElement(this.document[0])||(this.element.trigger("focus"),this.previous=e,this._delay(function(){this.previous=e}))}e=this.element[0]===V.ui.safeActiveElement(this.document[0])?this.previous:this.element.val(),t.preventDefault(),i.call(this),this.cancelBlur=!0,this._delay(function(){delete this.cancelBlur,i.call(this)}),!1!==this._start(t)&&this._repeat(null,V(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseup .ui-spinner-button":"_stop","mouseenter .ui-spinner-button":function(t){if(V(t.currentTarget).hasClass("ui-state-active"))return!1!==this._start(t)&&void this._repeat(null,V(t.currentTarget).hasClass("ui-spinner-up")?1:-1,t)},"mouseleave .ui-spinner-button":"_stop"},_enhance:function(){this.uiSpinner=this.element.attr("autocomplete","off").wrap("<span>").parent().append("<a></a><a></a>")},_draw:function(){this._enhance(),this._addClass(this.uiSpinner,"ui-spinner","ui-widget ui-widget-content"),this._addClass("ui-spinner-input"),this.element.attr("role","spinbutton"),this.buttons=this.uiSpinner.children("a").attr("tabIndex",-1).attr("aria-hidden",!0).button({classes:{"ui-button":""}}),this._removeClass(this.buttons,"ui-corner-all"),this._addClass(this.buttons.first(),"ui-spinner-button ui-spinner-up"),this._addClass(this.buttons.last(),"ui-spinner-button ui-spinner-down"),this.buttons.first().button({icon:this.options.icons.up,showLabel:!1}),this.buttons.last().button({icon:this.options.icons.down,showLabel:!1}),this.buttons.height()>Math.ceil(.5*this.uiSpinner.height())&&0<this.uiSpinner.height()&&this.uiSpinner.height(this.uiSpinner.height())},_keydown:function(t){var e=this.options,i=V.ui.keyCode;switch(t.keyCode){case i.UP:return this._repeat(null,1,t),!0;case i.DOWN:return this._repeat(null,-1,t),!0;case i.PAGE_UP:return this._repeat(null,e.page,t),!0;case i.PAGE_DOWN:return this._repeat(null,-e.page,t),!0}return!1},_start:function(t){return!(!this.spinning&&!1===this._trigger("start",t))&&(this.counter||(this.counter=1),this.spinning=!0)},_repeat:function(t,e,i){t=t||500,clearTimeout(this.timer),this.timer=this._delay(function(){this._repeat(40,e,i)},t),this._spin(e*this.options.step,i)},_spin:function(t,e){var i=this.value()||0;this.counter||(this.counter=1),i=this._adjustValue(i+t*this._increment(this.counter)),this.spinning&&!1===this._trigger("spin",e,{value:i})||(this._value(i),this.counter++)},_increment:function(t){var e=this.options.incremental;return e?"function"==typeof e?e(t):Math.floor(t*t*t/5e4-t*t/500+17*t/200+1):1},_precision:function(){var t=this._precisionOf(this.options.step);return t=null!==this.options.min?Math.max(t,this._precisionOf(this.options.min)):t},_precisionOf:function(t){var e=t.toString(),t=e.indexOf(".");return-1===t?0:e.length-t-1},_adjustValue:function(t){var e=this.options,i=null!==e.min?e.min:0,s=t-i;return t=i+Math.round(s/e.step)*e.step,t=parseFloat(t.toFixed(this._precision())),null!==e.max&&t>e.max?e.max:null!==e.min&&t<e.min?e.min:t},_stop:function(t){this.spinning&&(clearTimeout(this.timer),clearTimeout(this.mousewheelTimer),this.counter=0,this.spinning=!1,this._trigger("stop",t))},_setOption:function(t,e){var i;if("culture"===t||"numberFormat"===t)return i=this._parse(this.element.val()),this.options[t]=e,void this.element.val(this._format(i));"max"!==t&&"min"!==t&&"step"!==t||"string"==typeof e&&(e=this._parse(e)),"icons"===t&&(i=this.buttons.first().find(".ui-icon"),this._removeClass(i,null,this.options.icons.up),this._addClass(i,null,e.up),i=this.buttons.last().find(".ui-icon"),this._removeClass(i,null,this.options.icons.down),this._addClass(i,null,e.down)),this._super(t,e)},_setOptionDisabled:function(t){this._super(t),this._toggleClass(this.uiSpinner,null,"ui-state-disabled",!!t),this.element.prop("disabled",!!t),this.buttons.button(t?"disable":"enable")},_setOptions:ht(function(t){this._super(t)}),_parse:function(t){return""===(t="string"==typeof 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aria-valuenow"),this.uiSpinner.replaceWith(this.element)},stepUp:ht(function(t){this._stepUp(t)}),_stepUp:function(t){this._start()&&(this._spin((t||1)*this.options.step),this._stop())},stepDown:ht(function(t){this._stepDown(t)}),_stepDown:function(t){this._start()&&(this._spin((t||1)*-this.options.step),this._stop())},pageUp:ht(function(t){this._stepUp((t||1)*this.options.page)}),pageDown:ht(function(t){this._stepDown((t||1)*this.options.page)}),value:function(t){if(!arguments.length)return this._parse(this.element.val());ht(this._value).call(this,t)},widget:function(){return this.uiSpinner}}),!1!==V.uiBackCompat&&V.widget("ui.spinner",V.ui.spinner,{_enhance:function(){this.uiSpinner=this.element.attr("autocomplete","off").wrap(this._uiSpinnerHtml()).parent().append(this._buttonHtml())},_uiSpinnerHtml:function(){return"<span>"},_buttonHtml:function(){return"<a></a><a></a>"}});var 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t.altKey&&t.keyCode===V.ui.keyCode.PAGE_UP?(this._activate(this._focusNextTab(this.options.active-1,!1)),!0):t.altKey&&t.keyCode===V.ui.keyCode.PAGE_DOWN?(this._activate(this._focusNextTab(this.options.active+1,!0)),!0):void 0},_findNextTab:function(t,e){var i=this.tabs.length-1;for(;-1!==V.inArray(t=(t=i<t?0:t)<0?i:t,this.options.disabled);)t=e?t+1:t-1;return t},_focusNextTab:function(t,e){return t=this._findNextTab(t,e),this.tabs.eq(t).trigger("focus"),t},_setOption:function(t,e){"active"!==t?(this._super(t,e),"collapsible"===t&&(this._toggleClass("ui-tabs-collapsible",null,e),e||!1!==this.options.active||this._activate(0)),"event"===t&&this._setupEvents(e),"heightStyle"===t&&this._setupHeightStyle(e)):this._activate(e)},_sanitizeSelector:function(t){return t?t.replace(/[!"$%&'()*+,.\/:;<=>?@\[\]\^`{|}~]/g,"\\$&"):""},refresh:function(){var t=this.options,e=this.tablist.children(":has(a[href])");t.disabled=V.map(e.filter(".ui-state-disabled"),function(t){return e.index(t)}),this._processTabs(),!1!==t.active&&this.anchors.length?this.active.length&&!V.contains(this.tablist[0],this.active[0])?this.tabs.length===t.disabled.length?(t.active=!1,this.active=V()):this._activate(this._findNextTab(Math.max(0,t.active-1),!1)):t.active=this.tabs.index(this.active):(t.active=!1,this.active=V()),this._refresh()},_refresh:function(){this._setOptionDisabled(this.options.disabled),this._setupEvents(this.options.event),this._setupHeightStyle(this.options.heightStyle),this.tabs.not(this.active).attr({"aria-selected":"false","aria-expanded":"false",tabIndex:-1}),this.panels.not(this._getPanelForTab(this.active)).hide().attr({"aria-hidden":"true"}),this.active.length?(this.active.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0}),this._addClass(this.active,"ui-tabs-active","ui-state-active"),this._getPanelForTab(this.active).show().attr({"aria-hidden":"false"})):this.tabs.eq(0).attr("tabIndex",0)},_processTabs:function(){var l=this,t=this.tabs,e=this.anchors,i=this.panels;this.tablist=this._getList().attr("role","tablist"),this._addClass(this.tablist,"ui-tabs-nav","ui-helper-reset ui-helper-clearfix ui-widget-header"),this.tablist.on("mousedown"+this.eventNamespace,"> li",function(t){V(this).is(".ui-state-disabled")&&t.preventDefault()}).on("focus"+this.eventNamespace,".ui-tabs-anchor",function(){V(this).closest("li").is(".ui-state-disabled")&&this.blur()}),this.tabs=this.tablist.find("> li:has(a[href])").attr({role:"tab",tabIndex:-1}),this._addClass(this.tabs,"ui-tabs-tab","ui-state-default"),this.anchors=this.tabs.map(function(){return V("a",this)[0]}).attr({tabIndex:-1}),this._addClass(this.anchors,"ui-tabs-anchor"),this.panels=V(),this.anchors.each(function(t,e){var i,s,n,o=V(e).uniqueId().attr("id"),a=V(e).closest("li"),r=a.attr("aria-controls");l._isLocal(e)?(n=(i=e.hash).substring(1),s=l.element.find(l._sanitizeSelector(i))):(n=a.attr("aria-controls")||V({}).uniqueId()[0].id,(s=l.element.find(i="#"+n)).length||(s=l._createPanel(n)).insertAfter(l.panels[t-1]||l.tablist),s.attr("aria-live","polite")),s.length&&(l.panels=l.panels.add(s)),r&&a.data("ui-tabs-aria-controls",r),a.attr({"aria-controls":n,"aria-labelledby":o}),s.attr("aria-labelledby",o)}),this.panels.attr("role","tabpanel"),this._addClass(this.panels,"ui-tabs-panel","ui-widget-content"),t&&(this._off(t.not(this.tabs)),this._off(e.not(this.anchors)),this._off(i.not(this.panels)))},_getList:function(){return this.tablist||this.element.find("ol, ul").eq(0)},_createPanel:function(t){return V("<div>").attr("id",t).data("ui-tabs-destroy",!0)},_setOptionDisabled:function(t){var e,i;for(Array.isArray(t)&&(t.length?t.length===this.anchors.length&&(t=!0):t=!1),i=0;e=this.tabs[i];i++)e=V(e),!0===t||-1!==V.inArray(i,t)?(e.attr("aria-disabled","true"),this._addClass(e,null,"ui-state-disabled")):(e.removeAttr("aria-disabled"),this._removeClass(e,null,"ui-state-disabled"));this.options.disabled=t,this._toggleClass(this.widget(),this.widgetFullName+"-disabled",null,!0===t)},_setupEvents:function(t){var i={};t&&V.each(t.split(" "),function(t,e){i[e]="_eventHandler"}),this._off(this.anchors.add(this.tabs).add(this.panels)),this._on(!0,this.anchors,{click:function(t){t.preventDefault()}}),this._on(this.anchors,i),this._on(this.tabs,{keydown:"_tabKeydown"}),this._on(this.panels,{keydown:"_panelKeydown"}),this._focusable(this.tabs),this._hoverable(this.tabs)},_setupHeightStyle:function(t){var i,e=this.element.parent();"fill"===t?(i=e.height(),i-=this.element.outerHeight()-this.element.height(),this.element.siblings(":visible").each(function(){var t=V(this),e=t.css("position");"absolute"!==e&&"fixed"!==e&&(i-=t.outerHeight(!0))}),this.element.children().not(this.panels).each(function(){i-=V(this).outerHeight(!0)}),this.panels.each(function(){V(this).height(Math.max(0,i-V(this).innerHeight()+V(this).height()))}).css("overflow","auto")):"auto"===t&&(i=0,this.panels.each(function(){i=Math.max(i,V(this).height("").height())}).height(i))},_eventHandler:function(t){var e=this.options,i=this.active,s=V(t.currentTarget).closest("li"),n=s[0]===i[0],o=n&&e.collapsible,a=o?V():this._getPanelForTab(s),r=i.length?this._getPanelForTab(i):V(),i={oldTab:i,oldPanel:r,newTab:o?V():s,newPanel:a};t.preventDefault(),s.hasClass("ui-state-disabled")||s.hasClass("ui-tabs-loading")||this.running||n&&!e.collapsible||!1===this._trigger("beforeActivate",t,i)||(e.active=!o&&this.tabs.index(s),this.active=n?V():s,this.xhr&&this.xhr.abort(),r.length||a.length||V.error("jQuery UI Tabs: Mismatching fragment identifier."),a.length&&this.load(this.tabs.index(s),t),this._toggle(t,i))},_toggle:function(t,e){var i=this,s=e.newPanel,n=e.oldPanel;function o(){i.running=!1,i._trigger("activate",t,e)}function a(){i._addClass(e.newTab.closest("li"),"ui-tabs-active","ui-state-active"),s.length&&i.options.show?i._show(s,i.options.show,o):(s.show(),o())}this.running=!0,n.length&&this.options.hide?this._hide(n,this.options.hide,function(){i._removeClass(e.oldTab.closest("li"),"ui-tabs-active","ui-state-active"),a()}):(this._removeClass(e.oldTab.closest("li"),"ui-tabs-active","ui-state-active"),n.hide(),a()),n.attr("aria-hidden","true"),e.oldTab.attr({"aria-selected":"false","aria-expanded":"false"}),s.length&&n.length?e.oldTab.attr("tabIndex",-1):s.length&&this.tabs.filter(function(){return 0===V(this).attr("tabIndex")}).attr("tabIndex",-1),s.attr("aria-hidden","false"),e.newTab.attr({"aria-selected":"true","aria-expanded":"true",tabIndex:0})},_activate:function(t){var t=this._findActive(t);t[0]!==this.active[0]&&(t=(t=!t.length?this.active:t).find(".ui-tabs-anchor")[0],this._eventHandler({target:t,currentTarget:t,preventDefault:V.noop}))},_findActive:function(t){return!1===t?V():this.tabs.eq(t)},_getIndex:function(t){return t="string"==typeof t?this.anchors.index(this.anchors.filter("[href$='"+V.escapeSelector(t)+"']")):t},_destroy:function(){this.xhr&&this.xhr.abort(),this.tablist.removeAttr("role").off(this.eventNamespace),this.anchors.removeAttr("role tabIndex").removeUniqueId(),this.tabs.add(this.panels).each(function(){V.data(this,"ui-tabs-destroy")?V(this).remove():V(this).removeAttr("role tabIndex aria-live aria-busy aria-selected aria-labelledby aria-hidden aria-expanded")}),this.tabs.each(function(){var t=V(this),e=t.data("ui-tabs-aria-controls");e?t.attr("aria-controls",e).removeData("ui-tabs-aria-controls"):t.removeAttr("aria-controls")}),this.panels.show(),"content"!==this.options.heightStyle&&this.panels.css("height","")},enable:function(i){var t=this.options.disabled;!1!==t&&(t=void 0!==i&&(i=this._getIndex(i),Array.isArray(t)?V.map(t,function(t){return t!==i?t:null}):V.map(this.tabs,function(t,e){return e!==i?e:null})),this._setOptionDisabled(t))},disable:function(t){var e=this.options.disabled;if(!0!==e){if(void 0===t)e=!0;else{if(t=this._getIndex(t),-1!==V.inArray(t,e))return;e=Array.isArray(e)?V.merge([t],e).sort():[t]}this._setOptionDisabled(e)}},load:function(t,s){t=this._getIndex(t);function n(t,e){"abort"===e&&o.panels.stop(!1,!0),o._removeClass(i,"ui-tabs-loading"),a.removeAttr("aria-busy"),t===o.xhr&&delete o.xhr}var o=this,i=this.tabs.eq(t),t=i.find(".ui-tabs-anchor"),a=this._getPanelForTab(i),r={tab:i,panel:a};this._isLocal(t[0])||(this.xhr=V.ajax(this._ajaxSettings(t,s,r)),this.xhr&&"canceled"!==this.xhr.statusText&&(this._addClass(i,"ui-tabs-loading"),a.attr("aria-busy","true"),this.xhr.done(function(t,e,i){setTimeout(function(){a.html(t),o._trigger("load",s,r),n(i,e)},1)}).fail(function(t,e){setTimeout(function(){n(t,e)},1)})))},_ajaxSettings:function(t,i,s){var n=this;return{url:t.attr("href").replace(/#.*$/,""),beforeSend:function(t,e){return n._trigger("beforeLoad",i,V.extend({jqXHR:t,ajaxSettings:e},s))}}},_getPanelForTab:function(t){t=V(t).attr("aria-controls");return this.element.find(this._sanitizeSelector("#"+t))}}),!1!==V.uiBackCompat&&V.widget("ui.tabs",V.ui.tabs,{_processTabs:function(){this._superApply(arguments),this._addClass(this.tabs,"ui-tab")}});V.ui.tabs;V.widget("ui.tooltip",{version:"1.13.2",options:{classes:{"ui-tooltip":"ui-corner-all ui-widget-shadow"},content:function(){var t=V(this).attr("title");return V("<a>").text(t).html()},hide:!0,items:"[title]:not([disabled])",position:{my:"left top+15",at:"left bottom",collision:"flipfit flip"},show:!0,track:!1,close:null,open:null},_addDescribedBy:function(t,e){var i=(t.attr("aria-describedby")||"").split(/\s+/);i.push(e),t.data("ui-tooltip-id",e).attr("aria-describedby",String.prototype.trim.call(i.join(" ")))},_removeDescribedBy:function(t){var e=t.data("ui-tooltip-id"),i=(t.attr("aria-describedby")||"").split(/\s+/),e=V.inArray(e,i);-1!==e&&i.splice(e,1),t.removeData("ui-tooltip-id"),(i=String.prototype.trim.call(i.join(" ")))?t.attr("aria-describedby",i):t.removeAttr("aria-describedby")},_create:function(){this._on({mouseover:"open",focusin:"open"}),this.tooltips={},this.parents={},this.liveRegion=V("<div>").attr({role:"log","aria-live":"assertive","aria-relevant":"additions"}).appendTo(this.document[0].body),this._addClass(this.liveRegion,null,"ui-helper-hidden-accessible"),this.disabledTitles=V([])},_setOption:function(t,e){var i=this;this._super(t,e),"content"===t&&V.each(this.tooltips,function(t,e){i._updateContent(e.element)})},_setOptionDisabled:function(t){this[t?"_disable":"_enable"]()},_disable:function(){var s=this;V.each(this.tooltips,function(t,e){var i=V.Event("blur");i.target=i.currentTarget=e.element[0],s.close(i,!0)}),this.disabledTitles=this.disabledTitles.add(this.element.find(this.options.items).addBack().filter(function(){var t=V(this);if(t.is("[title]"))return t.data("ui-tooltip-title",t.attr("title")).removeAttr("title")}))},_enable:function(){this.disabledTitles.each(function(){var t=V(this);t.data("ui-tooltip-title")&&t.attr("title",t.data("ui-tooltip-title"))}),this.disabledTitles=V([])},open:function(t){var i=this,e=V(t?t.target:this.element).closest(this.options.items);e.length&&!e.data("ui-tooltip-id")&&(e.attr("title")&&e.data("ui-tooltip-title",e.attr("title")),e.data("ui-tooltip-open",!0),t&&"mouseover"===t.type&&e.parents().each(function(){var t,e=V(this);e.data("ui-tooltip-open")&&((t=V.Event("blur")).target=t.currentTarget=this,i.close(t,!0)),e.attr("title")&&(e.uniqueId(),i.parents[this.id]={element:this,title:e.attr("title")},e.attr("title",""))}),this._registerCloseHandlers(t,e),this._updateContent(e,t))},_updateContent:function(e,i){var t=this.options.content,s=this,n=i?i.type:null;if("string"==typeof t||t.nodeType||t.jquery)return this._open(i,e,t);(t=t.call(e[0],function(t){s._delay(function(){e.data("ui-tooltip-open")&&(i&&(i.type=n),this._open(i,e,t))})}))&&this._open(i,e,t)},_open:function(t,e,i){var s,n,o,a=V.extend({},this.options.position);function r(t){a.of=t,n.is(":hidden")||n.position(a)}i&&((s=this._find(e))?s.tooltip.find(".ui-tooltip-content").html(i):(e.is("[title]")&&(t&&"mouseover"===t.type?e.attr("title",""):e.removeAttr("title")),s=this._tooltip(e),n=s.tooltip,this._addDescribedBy(e,n.attr("id")),n.find(".ui-tooltip-content").html(i),this.liveRegion.children().hide(),(i=V("<div>").html(n.find(".ui-tooltip-content").html())).removeAttr("name").find("[name]").removeAttr("name"),i.removeAttr("id").find("[id]").removeAttr("id"),i.appendTo(this.liveRegion),this.options.track&&t&&/^mouse/.test(t.type)?(this._on(this.document,{mousemove:r}),r(t)):n.position(V.extend({of:e},this.options.position)),n.hide(),this._show(n,this.options.show),this.options.track&&this.options.show&&this.options.show.delay&&(o=this.delayedShow=setInterval(function(){n.is(":visible")&&(r(a.of),clearInterval(o))},13)),this._trigger("open",t,{tooltip:n})))},_registerCloseHandlers:function(t,e){var i={keyup:function(t){t.keyCode===V.ui.keyCode.ESCAPE&&((t=V.Event(t)).currentTarget=e[0],this.close(t,!0))}};e[0]!==this.element[0]&&(i.remove=function(){var t=this._find(e);t&&this._removeTooltip(t.tooltip)}),t&&"mouseover"!==t.type||(i.mouseleave="close"),t&&"focusin"!==t.type||(i.focusout="close"),this._on(!0,e,i)},close:function(t){var e,i=this,s=V(t?t.currentTarget:this.element),n=this._find(s);n?(e=n.tooltip,n.closing||(clearInterval(this.delayedShow),s.data("ui-tooltip-title")&&!s.attr("title")&&s.attr("title",s.data("ui-tooltip-title")),this._removeDescribedBy(s),n.hiding=!0,e.stop(!0),this._hide(e,this.options.hide,function(){i._removeTooltip(V(this))}),s.removeData("ui-tooltip-open"),this._off(s,"mouseleave focusout keyup"),s[0]!==this.element[0]&&this._off(s,"remove"),this._off(this.document,"mousemove"),t&&"mouseleave"===t.type&&V.each(this.parents,function(t,e){V(e.element).attr("title",e.title),delete i.parents[t]}),n.closing=!0,this._trigger("close",t,{tooltip:e}),n.hiding||(n.closing=!1))):s.removeData("ui-tooltip-open")},_tooltip:function(t){var e=V("<div>").attr("role","tooltip"),i=V("<div>").appendTo(e),s=e.uniqueId().attr("id");return this._addClass(i,"ui-tooltip-content"),this._addClass(e,"ui-tooltip","ui-widget ui-widget-content"),e.appendTo(this._appendTo(t)),this.tooltips[s]={element:t,tooltip:e}},_find:function(t){t=t.data("ui-tooltip-id");return t?this.tooltips[t]:null},_removeTooltip:function(t){clearInterval(this.delayedShow),t.remove(),delete this.tooltips[t.attr("id")]},_appendTo:function(t){t=t.closest(".ui-front, dialog");return t=!t.length?this.document[0].body:t},_destroy:function(){var s=this;V.each(this.tooltips,function(t,e){var i=V.Event("blur"),e=e.element;i.target=i.currentTarget=e[0],s.close(i,!0),V("#"+t).remove(),e.data("ui-tooltip-title")&&(e.attr("title")||e.attr("title",e.data("ui-tooltip-title")),e.removeData("ui-tooltip-title"))}),this.liveRegion.remove()}}),!1!==V.uiBackCompat&&V.widget("ui.tooltip",V.ui.tooltip,{options:{tooltipClass:null},_tooltip:function(){var t=this._superApply(arguments);return this.options.tooltipClass&&t.tooltip.addClass(this.options.tooltipClass),t}});V.ui.tooltip});</script>
<style type="text/css">

.tocify {
width: 20%;
max-height: 90%;
overflow: auto;
margin-left: 2%;
position: fixed;
border: 1px solid #ccc;
border-radius: 6px;
}

.tocify ul, .tocify li {
list-style: none;
margin: 0;
padding: 0;
border: none;
line-height: 30px;
}

.tocify-header {
text-indent: 10px;
}

.tocify-subheader {
text-indent: 20px;
display: none;
}

.tocify-subheader li {
font-size: 12px;
}

.tocify-subheader .tocify-subheader {
text-indent: 30px;
}
.tocify-subheader .tocify-subheader .tocify-subheader {
text-indent: 40px;
}
.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader {
text-indent: 50px;
}
.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader {
text-indent: 60px;
}

.tocify .tocify-item > a, .tocify .nav-list .nav-header {
margin: 0px;
}

.tocify .tocify-item a, .tocify .list-group-item {
padding: 5px;
}
.tocify .nav-pills > li {
float: none;
}


</style>
<script>/* jquery Tocify - v1.9.1 - 2013-10-22
 * http://www.gregfranko.com/jquery.tocify.js/
 * Copyright (c) 2013 Greg Franko; Licensed MIT */

// Immediately-Invoked Function Expression (IIFE) [Ben Alman Blog Post](http://benalman.com/news/2010/11/immediately-invoked-function-expression/) that calls another IIFE that contains all of the plugin logic.  I used this pattern so that anyone viewing this code would not have to scroll to the bottom of the page to view the local parameters that were passed to the main IIFE.
(function(tocify) {

    // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
    "use strict";

    // Calls the second IIFE and locally passes in the global jQuery, window, and document objects
    tocify(window.jQuery, window, document);

  }

  // Locally passes in `jQuery`, the `window` object, the `document` object, and an `undefined` variable.  The `jQuery`, `window` and `document` objects are passed in locally, to improve performance, since javascript first searches for a variable match within the local variables set before searching the global variables set.  All of the global variables are also passed in locally to be minifier friendly. `undefined` can be passed in locally, because it is not a reserved word in JavaScript.
  (function($, window, document, undefined) {

    // ECMAScript 5 Strict Mode: [John Resig Blog Post](http://ejohn.org/blog/ecmascript-5-strict-mode-json-and-more/)
    "use strict";

    var tocClassName = "tocify",
      tocClass = "." + tocClassName,
      tocFocusClassName = "tocify-focus",
      tocHoverClassName = "tocify-hover",
      hideTocClassName = "tocify-hide",
      hideTocClass = "." + hideTocClassName,
      headerClassName = "tocify-header",
      headerClass = "." + headerClassName,
      subheaderClassName = "tocify-subheader",
      subheaderClass = "." + subheaderClassName,
      itemClassName = "tocify-item",
      itemClass = "." + itemClassName,
      extendPageClassName = "tocify-extend-page",
      extendPageClass = "." + extendPageClassName;

    // Calling the jQueryUI Widget Factory Method
    $.widget("toc.tocify", {

      //Plugin version
      version: "1.9.1",

      // These options will be used as defaults
      options: {

        // **context**: Accepts String: Any jQuery selector
        // The container element that holds all of the elements used to generate the table of contents
        context: "body",

        // **ignoreSelector**: Accepts String: Any jQuery selector
        // A selector to any element that would be matched by selectors that you wish to be ignored
        ignoreSelector: null,

        // **selectors**: Accepts an Array of Strings: Any jQuery selectors
        // The element's used to generate the table of contents.  The order is very important since it will determine the table of content's nesting structure
        selectors: "h1, h2, h3",

        // **showAndHide**: Accepts a boolean: true or false
        // Used to determine if elements should be shown and hidden
        showAndHide: true,

        // **showEffect**: Accepts String: "none", "fadeIn", "show", or "slideDown"
        // Used to display any of the table of contents nested items
        showEffect: "slideDown",

        // **showEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the show animation
        showEffectSpeed: "medium",

        // **hideEffect**: Accepts String: "none", "fadeOut", "hide", or "slideUp"
        // Used to hide any of the table of contents nested items
        hideEffect: "slideUp",

        // **hideEffectSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the hide animation
        hideEffectSpeed: "medium",

        // **smoothScroll**: Accepts a boolean: true or false
        // Determines if a jQuery animation should be used to scroll to specific table of contents items on the page
        smoothScroll: true,

        // **smoothScrollSpeed**: Accepts Number (milliseconds) or String: "slow", "medium", or "fast"
        // The time duration of the smoothScroll animation
        smoothScrollSpeed: "medium",

        // **scrollTo**: Accepts Number (pixels)
        // The amount of space between the top of page and the selected table of contents item after the page has been scrolled
        scrollTo: 0,

        // **showAndHideOnScroll**: Accepts a boolean: true or false
        // Determines if table of contents nested items should be shown and hidden while scrolling
        showAndHideOnScroll: true,

        // **highlightOnScroll**: Accepts a boolean: true or false
        // Determines if table of contents nested items should be highlighted (set to a different color) while scrolling
        highlightOnScroll: true,

        // **highlightOffset**: Accepts a number
        // The offset distance in pixels to trigger the next active table of contents item
        highlightOffset: 40,

        // **theme**: Accepts a string: "bootstrap", "jqueryui", or "none"
        // Determines if Twitter Bootstrap, jQueryUI, or Tocify classes should be added to the table of contents
        theme: "bootstrap",

        // **extendPage**: Accepts a boolean: true or false
        // If a user scrolls to the bottom of the page and the page is not tall enough to scroll to the last table of contents item, then the page height is increased
        extendPage: true,

        // **extendPageOffset**: Accepts a number: pixels
        // How close to the bottom of the page a user must scroll before the page is extended
        extendPageOffset: 100,

        // **history**: Accepts a boolean: true or false
        // Adds a hash to the page url to maintain history
        history: true,

        // **scrollHistory**: Accepts a boolean: true or false
        // Adds a hash to the page url, to maintain history, when scrolling to a TOC item
        scrollHistory: false,

        // **hashGenerator**: How the hash value (the anchor segment of the URL, following the
        // # character) will be generated.
        //
        // "compact" (default) - #CompressesEverythingTogether
        // "pretty" - #looks-like-a-nice-url-and-is-easily-readable
        // function(text, element){} - Your own hash generation function that accepts the text as an
        // argument, and returns the hash value.
        hashGenerator: "compact",

        // **highlightDefault**: Accepts a boolean: true or false
        // Set's the first TOC item as active if no other TOC item is active.
        highlightDefault: true

      },

      // _Create
      // -------
      //      Constructs the plugin.  Only called once.
      _create: function() {

        var self = this;

        self.extendPageScroll = true;

        // Internal array that keeps track of all TOC items (Helps to recognize if there are duplicate TOC item strings)
        self.items = [];

        // Generates the HTML for the dynamic table of contents
        self._generateToc();

        // Adds CSS classes to the newly generated table of contents HTML
        self._addCSSClasses();

        self.webkit = (function() {

          for (var prop in window) {

            if (prop) {

              if (prop.toLowerCase().indexOf("webkit") !== -1) {

                return true;

              }

            }

          }

          return false;

        }());

        // Adds jQuery event handlers to the newly generated table of contents
        self._setEventHandlers();

        // Binding to the Window load event to make sure the correct scrollTop is calculated
        $(window).on("load", function() {

          // Sets the active TOC item
          self._setActiveElement(true);

          // Once all animations on the page are complete, this callback function will be called
          $("html, body").promise().done(function() {

            setTimeout(function() {

              self.extendPageScroll = false;

            }, 0);

          });

        });

      },

      // _generateToc
      // ------------
      //      Generates the HTML for the dynamic table of contents
      _generateToc: function() {

        // _Local variables_

        // Stores the plugin context in the self variable
        var self = this,

          // All of the HTML tags found within the context provided (i.e. body) that match the top level jQuery selector above
          firstElem,

          // Instantiated variable that will store the top level newly created unordered list DOM element
          ul,
          ignoreSelector = self.options.ignoreSelector;


        // Determine the element to start the toc with
        // get all the top level selectors
        firstElem = [];
        var selectors = this.options.selectors.replace(/ /g, "").split(",");
        // find the first set that have at least one non-ignored element
        for(var i = 0; i < selectors.length; i++) {
          var foundSelectors = $(this.options.context).find(selectors[i]);
          for (var s = 0; s < foundSelectors.length; s++) {
            if (!$(foundSelectors[s]).is(ignoreSelector)) {
              firstElem = foundSelectors;
              break;
            }
          }
          if (firstElem.length> 0)
            break;
        }

        if (!firstElem.length) {

          self.element.addClass(hideTocClassName);

          return;

        }

        self.element.addClass(tocClassName);

        // Loops through each top level selector
        firstElem.each(function(index) {

          //If the element matches the ignoreSelector then we skip it
          if ($(this).is(ignoreSelector)) {
            return;
          }

          // Creates an unordered list HTML element and adds a dynamic ID and standard class name
          ul = $("<ul/>", {
            "id": headerClassName + index,
            "class": headerClassName
          }).

          // Appends a top level list item HTML element to the previously created HTML header
          append(self._nestElements($(this), index));

          // Add the created unordered list element to the HTML element calling the plugin
          self.element.append(ul);

          // Finds all of the HTML tags between the header and subheader elements
          $(this).nextUntil(this.nodeName.toLowerCase()).each(function() {

            // If there are no nested subheader elemements
            if ($(this).find(self.options.selectors).length === 0) {

              // Loops through all of the subheader elements
              $(this).filter(self.options.selectors).each(function() {

                //If the element matches the ignoreSelector then we skip it
                if ($(this).is(ignoreSelector)) {
                  return;
                }

                self._appendSubheaders.call(this, self, ul);

              });

            }

            // If there are nested subheader elements
            else {

              // Loops through all of the subheader elements
              $(this).find(self.options.selectors).each(function() {

                //If the element matches the ignoreSelector then we skip it
                if ($(this).is(ignoreSelector)) {
                  return;
                }

                self._appendSubheaders.call(this, self, ul);

              });

            }

          });

        });

      },

      _setActiveElement: function(pageload) {

        var self = this,

          hash = window.location.hash.substring(1),

          elem = self.element.find('li[data-unique="' + hash + '"]');

        if (hash.length) {

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          // Highlights the current list item that was clicked
          elem.addClass(self.focusClass);

          // Triggers the click event on the currently focused TOC item
          elem.click();

        } else {

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          if (!hash.length && pageload && self.options.highlightDefault) {

            // Highlights the first TOC item if no other items are highlighted
            self.element.find(itemClass).first().addClass(self.focusClass);

          }

        }

        return self;

      },

      // _nestElements
      // -------------
      //      Helps create the table of contents list by appending nested list items
      _nestElements: function(self, index) {

        var arr, item, hashValue;

        arr = $.grep(this.items, function(item) {

          return item === self.text();

        });

        // If there is already a duplicate TOC item
        if (arr.length) {

          // Adds the current TOC item text and index (for slight randomization) to the internal array
          this.items.push(self.text() + index);

        }

        // If there not a duplicate TOC item
        else {

          // Adds the current TOC item text to the internal array
          this.items.push(self.text());

        }

        hashValue = this._generateHashValue(arr, self, index);

        // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
        item = $("<li/>", {

          // Sets a common class name to the list item
          "class": itemClassName,

          "data-unique": hashValue

        });

        if (this.options.theme !== "bootstrap3") {

          item.append($("<a/>", {

            "html": self.html()

          }));

        } else {

          item.html(self.html());

        }

        // Adds an HTML anchor tag before the currently traversed HTML element
        self.before($("<div/>", {

          // Sets a name attribute on the anchor tag to the text of the currently traversed HTML element (also making sure that all whitespace is replaced with an underscore)
          "name": hashValue,

          "data-unique": hashValue

        }));

        return item;

      },

      // _generateHashValue
      // ------------------
      //      Generates the hash value that will be used to refer to each item.
      _generateHashValue: function(arr, self, index) {

        var hashValue = "",
          hashGeneratorOption = this.options.hashGenerator;

        if (hashGeneratorOption === "pretty") {

          // prettify the text
          hashValue = self.text().toLowerCase().replace(/\s/g, "-");

          // fix double hyphens
          while (hashValue.indexOf("--") > -1) {
            hashValue = hashValue.replace(/--/g, "-");
          }

          // fix colon-space instances
          while (hashValue.indexOf(":-") > -1) {
            hashValue = hashValue.replace(/:-/g, "-");
          }

        } else if (typeof hashGeneratorOption === "function") {

          // call the function
          hashValue = hashGeneratorOption(self.text(), self);

        } else {

          // compact - the default
          hashValue = self.text().replace(/\s/g, "");

        }

        // add the index if we need to
        if (arr.length) {
          hashValue += "" + index;
        }

        // return the value
        return hashValue;

      },

      // _appendElements
      // ---------------
      //      Helps create the table of contents list by appending subheader elements

      _appendSubheaders: function(self, ul) {

        // The current element index
        var index = $(this).index(self.options.selectors),

          // Finds the previous header DOM element
          previousHeader = $(self.options.selectors).eq(index - 1),

          currentTagName = +$(this).prop("tagName").charAt(1),

          previousTagName = +previousHeader.prop("tagName").charAt(1),

          lastSubheader;

        // If the current header DOM element is smaller than the previous header DOM element or the first subheader
        if (currentTagName < previousTagName) {

          // Selects the last unordered list HTML found within the HTML element calling the plugin
          self.element.find(subheaderClass + "[data-tag=" + currentTagName + "]").last().append(self._nestElements($(this), index));

        }

        // If the current header DOM element is the same type of header(eg. h4) as the previous header DOM element
        else if (currentTagName === previousTagName) {

          ul.find(itemClass).last().after(self._nestElements($(this), index));

        } else {

          // Selects the last unordered list HTML found within the HTML element calling the plugin
          ul.find(itemClass).last().

          // Appends an unorderedList HTML element to the dynamic `unorderedList` variable and sets a common class name
          after($("<ul/>", {

            "class": subheaderClassName,

            "data-tag": currentTagName

          })).next(subheaderClass).

          // Appends a list item HTML element to the last unordered list HTML element found within the HTML element calling the plugin
          append(self._nestElements($(this), index));
        }

      },

      // _setEventHandlers
      // ----------------
      //      Adds jQuery event handlers to the newly generated table of contents
      _setEventHandlers: function() {

        // _Local variables_

        // Stores the plugin context in the self variable
        var self = this,

          // Instantiates a new variable that will be used to hold a specific element's context
          $self,

          // Instantiates a new variable that will be used to determine the smoothScroll animation time duration
          duration;

        // Event delegation that looks for any clicks on list item elements inside of the HTML element calling the plugin
        this.element.on("click.tocify", "li", function(event) {

          if (self.options.history) {

            window.location.hash = $(this).attr("data-unique");

          }

          // Removes highlighting from all of the list item's
          self.element.find("." + self.focusClass).removeClass(self.focusClass);

          // Highlights the current list item that was clicked
          $(this).addClass(self.focusClass);

          // If the showAndHide option is true
          if (self.options.showAndHide) {

            var elem = $('li[data-unique="' + $(this).attr("data-unique") + '"]');

            self._triggerShow(elem);

          }

          self._scrollTo($(this));

        });

        // Mouseenter and Mouseleave event handlers for the list item's within the HTML element calling the plugin
        this.element.find("li").on({

          // Mouseenter event handler
          "mouseenter.tocify": function() {

            // Adds a hover CSS class to the current list item
            $(this).addClass(self.hoverClass);

            // Makes sure the cursor is set to the pointer icon
            $(this).css("cursor", "pointer");

          },

          // Mouseleave event handler
          "mouseleave.tocify": function() {

            if (self.options.theme !== "bootstrap") {

              // Removes the hover CSS class from the current list item
              $(this).removeClass(self.hoverClass);

            }

          }
        });

        // only attach handler if needed (expensive in IE)
        if (self.options.extendPage || self.options.highlightOnScroll || self.options.scrollHistory || self.options.showAndHideOnScroll) {
          // Window scroll event handler
          $(window).on("scroll.tocify", function() {

            // Once all animations on the page are complete, this callback function will be called
            $("html, body").promise().done(function() {

              // Local variables

              // Stores how far the user has scrolled
              var winScrollTop = $(window).scrollTop(),

                // Stores the height of the window
                winHeight = $(window).height(),

                // Stores the height of the document
                docHeight = $(document).height(),

                scrollHeight = $("body")[0].scrollHeight,

                // Instantiates a variable that will be used to hold a selected HTML element
                elem,

                lastElem,

                lastElemOffset,

                currentElem;

              if (self.options.extendPage) {

                // If the user has scrolled to the bottom of the page and the last toc item is not focused
                if ((self.webkit && winScrollTop >= scrollHeight - winHeight - self.options.extendPageOffset) || (!self.webkit && winHeight + winScrollTop > docHeight - self.options.extendPageOffset)) {

                  if (!$(extendPageClass).length) {

                    lastElem = $('div[data-unique="' + $(itemClass).last().attr("data-unique") + '"]');

                    if (!lastElem.length) return;

                    // Gets the top offset of the page header that is linked to the last toc item
                    lastElemOffset = lastElem.offset().top;

                    // Appends a div to the bottom of the page and sets the height to the difference of the window scrollTop and the last element's position top offset
                    $(self.options.context).append($("<div/>", {

                      "class": extendPageClassName,

                      "height": Math.abs(lastElemOffset - winScrollTop) + "px",

                      "data-unique": extendPageClassName

                    }));

                    if (self.extendPageScroll) {

                      currentElem = self.element.find('li.' + self.focusClass);

                      self._scrollTo($('div[data-unique="' + currentElem.attr("data-unique") + '"]'));

                    }

                  }

                }

              }

              // The zero timeout ensures the following code is run after the scroll events
              setTimeout(function() {

                // _Local variables_

                // Stores the distance to the closest anchor
                var closestAnchorDistance = null,

                  // Stores the index of the closest anchor
                  closestAnchorIdx = null,

                  // Keeps a reference to all anchors
                  anchors = $(self.options.context).find("div[data-unique]"),

                  anchorText;

                // Determines the index of the closest anchor
                anchors.each(function(idx) {
                  var distance = Math.abs(($(this).next().length ? $(this).next() : $(this)).offset().top - winScrollTop - self.options.highlightOffset);
                  if (closestAnchorDistance == null || distance < closestAnchorDistance) {
                    closestAnchorDistance = distance;
                    closestAnchorIdx = idx;
                  } else {
                    return false;
                  }
                });

                anchorText = $(anchors[closestAnchorIdx]).attr("data-unique");

                // Stores the list item HTML element that corresponds to the currently traversed anchor tag
                elem = $('li[data-unique="' + anchorText + '"]');

                // If the `highlightOnScroll` option is true and a next element is found
                if (self.options.highlightOnScroll && elem.length) {

                  // Removes highlighting from all of the list item's
                  self.element.find("." + self.focusClass).removeClass(self.focusClass);

                  // Highlights the corresponding list item
                  elem.addClass(self.focusClass);

                }

                if (self.options.scrollHistory) {

                  if (window.location.hash !== "#" + anchorText) {

                    window.location.replace("#" + anchorText);

                  }
                }

                // If the `showAndHideOnScroll` option is true
                if (self.options.showAndHideOnScroll && self.options.showAndHide) {

                  self._triggerShow(elem, true);

                }

              }, 0);

            });

          });
        }

      },

      // Show
      // ----
      //      Opens the current sub-header
      show: function(elem, scroll) {

        // Stores the plugin context in the `self` variable
        var self = this,
          element = elem;

        // If the sub-header is not already visible
        if (!elem.is(":visible")) {

          // If the current element does not have any nested subheaders, is not a header, and its parent is not visible
          if (!elem.find(subheaderClass).length && !elem.parent().is(headerClass) && !elem.parent().is(":visible")) {

            // Sets the current element to all of the subheaders within the current header
            elem = elem.parents(subheaderClass).add(elem);

          }

          // If the current element does not have any nested subheaders and is not a header
          else if (!elem.children(subheaderClass).length && !elem.parent().is(headerClass)) {

            // Sets the current element to the closest subheader
            elem = elem.closest(subheaderClass);

          }

          //Determines what jQuery effect to use
          switch (self.options.showEffect) {

            //Uses `no effect`
            case "none":

              elem.show();

              break;

              //Uses the jQuery `show` special effect
            case "show":

              elem.show(self.options.showEffectSpeed);

              break;

              //Uses the jQuery `slideDown` special effect
            case "slideDown":

              elem.slideDown(self.options.showEffectSpeed);

              break;

              //Uses the jQuery `fadeIn` special effect
            case "fadeIn":

              elem.fadeIn(self.options.showEffectSpeed);

              break;

              //If none of the above options were passed, then a `jQueryUI show effect` is expected
            default:

              elem.show();

              break;

          }

        }

        // If the current subheader parent element is a header
        if (elem.parent().is(headerClass)) {

          // Hides all non-active sub-headers
          self.hide($(subheaderClass).not(elem));

        }

        // If the current subheader parent element is not a header
        else {

          // Hides all non-active sub-headers
          self.hide($(subheaderClass).not(elem.closest(headerClass).find(subheaderClass).not(elem.siblings())));

        }

        // Maintains chainablity
        return self;

      },

      // Hide
      // ----
      //      Closes the current sub-header
      hide: function(elem) {

        // Stores the plugin context in the `self` variable
        var self = this;

        //Determines what jQuery effect to use
        switch (self.options.hideEffect) {

          // Uses `no effect`
          case "none":

            elem.hide();

            break;

            // Uses the jQuery `hide` special effect
          case "hide":

            elem.hide(self.options.hideEffectSpeed);

            break;

            // Uses the jQuery `slideUp` special effect
          case "slideUp":

            elem.slideUp(self.options.hideEffectSpeed);

            break;

            // Uses the jQuery `fadeOut` special effect
          case "fadeOut":

            elem.fadeOut(self.options.hideEffectSpeed);

            break;

            // If none of the above options were passed, then a `jqueryUI hide effect` is expected
          default:

            elem.hide();

            break;

        }

        // Maintains chainablity
        return self;
      },

      // _triggerShow
      // ------------
      //      Determines what elements get shown on scroll and click
      _triggerShow: function(elem, scroll) {

        var self = this;

        // If the current element's parent is a header element or the next element is a nested subheader element
        if (elem.parent().is(headerClass) || elem.next().is(subheaderClass)) {

          // Shows the next sub-header element
          self.show(elem.next(subheaderClass), scroll);

        }

        // If the current element's parent is a subheader element
        else if (elem.parent().is(subheaderClass)) {

          // Shows the parent sub-header element
          self.show(elem.parent(), scroll);

        }

        // Maintains chainability
        return self;

      },

      // _addCSSClasses
      // --------------
      //      Adds CSS classes to the newly generated table of contents HTML
      _addCSSClasses: function() {

        // If the user wants a jqueryUI theme
        if (this.options.theme === "jqueryui") {

          this.focusClass = "ui-state-default";

          this.hoverClass = "ui-state-hover";

          //Adds the default styling to the dropdown list
          this.element.addClass("ui-widget").find(".toc-title").addClass("ui-widget-header").end().find("li").addClass("ui-widget-content");

        }

        // If the user wants a twitterBootstrap theme
        else if (this.options.theme === "bootstrap") {

          this.element.find(headerClass + "," + subheaderClass).addClass("nav nav-list");

          this.focusClass = "active";

        }

        // If the user wants a twitterBootstrap theme
        else if (this.options.theme === "bootstrap3") {

          this.element.find(headerClass + "," + subheaderClass).addClass("list-group");

          this.element.find(itemClass).addClass("list-group-item");

          this.focusClass = "active";

        }

        // If a user does not want a prebuilt theme
        else {

          // Adds more neutral classes (instead of jqueryui)

          this.focusClass = tocFocusClassName;

          this.hoverClass = tocHoverClassName;

        }

        //Maintains chainability
        return this;

      },

      // setOption
      // ---------
      //      Sets a single Tocify option after the plugin is invoked
      setOption: function() {

        // Calls the jQueryUI Widget Factory setOption method
        $.Widget.prototype._setOption.apply(this, arguments);

      },

      // setOptions
      // ----------
      //      Sets a single or multiple Tocify options after the plugin is invoked
      setOptions: function() {

        // Calls the jQueryUI Widget Factory setOptions method
        $.Widget.prototype._setOptions.apply(this, arguments);

      },

      // _scrollTo
      // ---------
      //      Scrolls to a specific element
      _scrollTo: function(elem) {

        var self = this,
          duration = self.options.smoothScroll || 0,
          scrollTo = self.options.scrollTo,
          currentDiv = $('div[data-unique="' + elem.attr("data-unique") + '"]');

        if (!currentDiv.length) {

          return self;

        }

        // Once all animations on the page are complete, this callback function will be called
        $("html, body").promise().done(function() {

          // Animates the html and body element scrolltops
          $("html, body").animate({

            // Sets the jQuery `scrollTop` to the top offset of the HTML div tag that matches the current list item's `data-unique` tag
            "scrollTop": currentDiv.offset().top - ($.isFunction(scrollTo) ? scrollTo.call() : scrollTo) + "px"

          }, {

            // Sets the smoothScroll animation time duration to the smoothScrollSpeed option
            "duration": duration

          });

        });

        // Maintains chainability
        return self;

      }

    });

  })); //end of plugin
</script>
<script>

/**
 * jQuery Plugin: Sticky Tabs
 *
 * @author Aidan Lister <aidan@php.net>
 * adapted by Ruben Arslan to activate parent tabs too
 * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/
 */
(function($) {
  "use strict";
  $.fn.rmarkdownStickyTabs = function() {
    var context = this;
    // Show the tab corresponding with the hash in the URL, or the first tab
    var showStuffFromHash = function() {
      var hash = window.location.hash;
      var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a';
      var $selector = $(selector, context);
      if($selector.data('toggle') === "tab") {
        $selector.tab('show');
        // walk up the ancestors of this element, show any hidden tabs
        $selector.parents('.section.tabset').each(function(i, elm) {
          var link = $('a[href="#' + $(elm).attr('id') + '"]');
          if(link.data('toggle') === "tab") {
            link.tab("show");
          }
        });
      }
    };


    // Set the correct tab when the page loads
    showStuffFromHash(context);

    // Set the correct tab when a user uses their back/forward button
    $(window).on('hashchange', function() {
      showStuffFromHash(context);
    });

    // Change the URL when tabs are clicked
    $('a', context).on('click', function(e) {
      history.pushState(null, null, this.href);
      showStuffFromHash(context);
    });

    return this;
  };
}(jQuery));

window.buildTabsets = function(tocID) {

  // build a tabset from a section div with the .tabset class
  function buildTabset(tabset) {

    // check for fade and pills options
    var fade = tabset.hasClass("tabset-fade");
    var pills = tabset.hasClass("tabset-pills");
    var navClass = pills ? "nav-pills" : "nav-tabs";

    // determine the heading level of the tabset and tabs
    var match = tabset.attr('class').match(/level(\d) /);
    if (match === null)
      return;
    var tabsetLevel = Number(match[1]);
    var tabLevel = tabsetLevel + 1;

    // find all subheadings immediately below
    var tabs = tabset.find("div.section.level" + tabLevel);
    if (!tabs.length)
      return;

    // create tablist and tab-content elements
    var tabList = $('<ul class="nav ' + navClass + '" role="tablist"></ul>');
    $(tabs[0]).before(tabList);
    var tabContent = $('<div class="tab-content"></div>');
    $(tabs[0]).before(tabContent);

    // build the tabset
    var activeTab = 0;
    tabs.each(function(i) {

      // get the tab div
      var tab = $(tabs[i]);

      // get the id then sanitize it for use with bootstrap tabs
      var id = tab.attr('id');

      // see if this is marked as the active tab
      if (tab.hasClass('active'))
        activeTab = i;

      // remove any table of contents entries associated with
      // this ID (since we'll be removing the heading element)
      $("div#" + tocID + " li a[href='#" + id + "']").parent().remove();

      // sanitize the id for use with bootstrap tabs
      id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_');
      tab.attr('id', id);

      // get the heading element within it, grab it's text, then remove it
      var heading = tab.find('h' + tabLevel + ':first');
      var headingText = heading.html();
      heading.remove();

      // build and append the tab list item
      var a = $('<a role="tab" data-toggle="tab">' + headingText + '</a>');
      a.attr('href', '#' + id);
      a.attr('aria-controls', id);
      var li = $('<li role="presentation"></li>');
      li.append(a);
      tabList.append(li);

      // set it's attributes
      tab.attr('role', 'tabpanel');
      tab.addClass('tab-pane');
      tab.addClass('tabbed-pane');
      if (fade)
        tab.addClass('fade');

      // move it into the tab content div
      tab.detach().appendTo(tabContent);
    });

    // set active tab
    $(tabList.children('li')[activeTab]).addClass('active');
    var active = $(tabContent.children('div.section')[activeTab]);
    active.addClass('active');
    if (fade)
      active.addClass('in');

    if (tabset.hasClass("tabset-sticky"))
      tabset.rmarkdownStickyTabs();
  }

  // convert section divs with the .tabset class to tabsets
  var tabsets = $("div.section.tabset");
  tabsets.each(function(i) {
    buildTabset($(tabsets[i]));
  });
};

</script>
<script>
window.initializeCodeFolding = function(show) {

  // handlers for show-all and hide all
  $("#rmd-show-all-code").click(function() {
    $('div.r-code-collapse').each(function() {
      $(this).collapse('show');
    });
  });
  $("#rmd-hide-all-code").click(function() {
    $('div.r-code-collapse').each(function() {
      $(this).collapse('hide');
    });
  });

  // index for unique code element ids
  var currentIndex = 1;

  // select all R code blocks
  var rCodeBlocks = $('pre.r, pre.python, pre.bash, pre.sql, pre.cpp, pre.stan, pre.julia, pre.foldable');
  rCodeBlocks.each(function() {
    // skip if the block has fold-none class
    if ($(this).hasClass('fold-none')) return;

    // create a collapsable div to wrap the code in
    var div = $('<div class="collapse r-code-collapse"></div>');
    var showThis = (show || $(this).hasClass('fold-show')) && !$(this).hasClass('fold-hide');
    var id = 'rcode-643E0F36' + currentIndex++;
    div.attr('id', id);
    $(this).before(div);
    $(this).detach().appendTo(div);

    // add a show code button right above
    var showCodeText = $('<span>' + (showThis ? 'Hide' : 'Show') + '</span>');
    var showCodeButton = $('<button type="button" class="btn btn-default btn-xs btn-secondary btn-sm code-folding-btn pull-right float-right"></button>');
    showCodeButton.append(showCodeText);
    showCodeButton
        .attr('data-toggle', 'collapse')
        .attr('data-bs-toggle', 'collapse') // BS5
        .attr('data-target', '#' + id)
        .attr('data-bs-target', '#' + id)   // BS5
        .attr('aria-expanded', showThis)
        .attr('aria-controls', id);

    var buttonRow = $('<div class="row"></div>');
    var buttonCol = $('<div class="col-md-12"></div>');

    buttonCol.append(showCodeButton);
    buttonRow.append(buttonCol);

    div.before(buttonRow);

    // show the div if necessary
    if (showThis) div.collapse('show');

    // update state of button on show/hide
    //   * Change text
    //   * add a class for intermediate states styling
    div.on('hide.bs.collapse', function () {
      showCodeText.text('Show');
      showCodeButton.addClass('btn-collapsing');
    });
    div.on('hidden.bs.collapse', function () {
      showCodeButton.removeClass('btn-collapsing');
    });
    div.on('show.bs.collapse', function () {
      showCodeText.text('Hide');
      showCodeButton.addClass('btn-expanding');
    });
    div.on('shown.bs.collapse', function () {
      showCodeButton.removeClass('btn-expanding');
    });

  });

}
</script>
<style type="text/css">.pagedtable {
overflow: auto;
padding-left: 8px;
padding-right: 8px;
}
.pagedtable-wrapper {
border: 1px solid #ccc;
border-radius: 4px;
margin-bottom: 10px;
}
.pagedtable table {
width: 100%;
max-width: 100%;
margin: 0;
}
.pagedtable th {
padding: 0 5px 0 5px;
border: none;
border-bottom: 2px solid #dddddd;
min-width: 45px;
}
.pagedtable-empty th {
display: none;
}
.pagedtable td {
padding: 0 4px 0 4px;
}
.pagedtable .even {
background-color: rgba(140, 140, 140, 0.1);
}
.pagedtable-padding-col {
display: none;
}
.pagedtable a {
-webkit-touch-callout: none;
-webkit-user-select: none;
-khtml-user-select: none;
-moz-user-select: none;
-ms-user-select: none;
user-select: none;
}
.pagedtable-index-nav {
cursor: pointer;
padding: 0 5px 0 5px;
float: right;
border: 0;
}
.pagedtable-index-nav-disabled {
cursor: default;
text-decoration: none;
color: #999;
}
a.pagedtable-index-nav-disabled:hover {
text-decoration: none;
color: #999;
}
.pagedtable-indexes {
cursor: pointer;
float: right;
border: 0;
}
.pagedtable-index-current {
cursor: default;
text-decoration: none;
font-weight: bold;
color: #333;
border: 0;
}
a.pagedtable-index-current:hover {
text-decoration: none;
font-weight: bold;
color: #333;
}
.pagedtable-index {
width: 30px;
display: inline-block;
text-align: center;
border: 0;
}
.pagedtable-index-separator-left {
display: inline-block;
color: #333;
font-size: 9px;
padding: 0 0 0 0;
cursor: default;
}
.pagedtable-index-separator-right {
display: inline-block;
color: #333;
font-size: 9px;
padding: 0 4px 0 0;
cursor: default;
}
.pagedtable-footer {
padding-top: 4px;
padding-bottom: 5px;
}
.pagedtable-not-empty .pagedtable-footer {
border-top: 2px solid #dddddd;
}
.pagedtable-info {
overflow: hidden;
color: #999;
white-space: nowrap;
text-overflow: ellipsis;
}
.pagedtable-header-name {
overflow: hidden;
text-overflow: ellipsis;
}
.pagedtable-header-type {
color: #999;
font-weight: 400;
}
.pagedtable-na-cell {
font-style: italic;
opacity: 0.3;
}
</style>
<script>// Production steps of ECMA-262, Edition 5, 15.4.4.18
// Reference: http://es5.github.io/#x15.4.4.18
if (!Array.prototype.forEach) {

  Array.prototype.forEach = function(callback, thisArg) {

    var T, k;

    if (this === null) {
      throw new TypeError(' this is null or not defined');
    }

    // 1. Let O be the result of calling toObject() passing the
    // |this| value as the argument.
    var O = Object(this);

    // 2. Let lenValue be the result of calling the Get() internal
    // method of O with the argument "length".
    // 3. Let len be toUint32(lenValue).
    var len = O.length >>> 0;

    // 4. If isCallable(callback) is false, throw a TypeError exception.
    // See: http://es5.github.com/#x9.11
    if (typeof callback !== "function") {
      throw new TypeError(callback + ' is not a function');
    }

    // 5. If thisArg was supplied, let T be thisArg; else let
    // T be undefined.
    if (arguments.length > 1) {
      T = thisArg;
    }

    // 6. Let k be 0
    k = 0;

    // 7. Repeat, while k < len
    while (k < len) {

      var kValue;

      // a. Let Pk be ToString(k).
      //    This is implicit for LHS operands of the in operator
      // b. Let kPresent be the result of calling the HasProperty
      //    internal method of O with argument Pk.
      //    This step can be combined with c
      // c. If kPresent is true, then
      if (k in O) {

        // i. Let kValue be the result of calling the Get internal
        // method of O with argument Pk.
        kValue = O[k];

        // ii. Call the Call internal method of callback with T as
        // the this value and argument list containing kValue, k, and O.
        callback.call(T, kValue, k, O);
      }
      // d. Increase k by 1.
      k++;
    }
    // 8. return undefined
  };
}

// Production steps of ECMA-262, Edition 5, 15.4.4.19
// Reference: http://es5.github.io/#x15.4.4.19
if (!Array.prototype.map) {

  Array.prototype.map = function(callback, thisArg) {

    var T, A, k;

    if (this == null) {
      throw new TypeError(' this is null or not defined');
    }

    // 1. Let O be the result of calling ToObject passing the |this|
    //    value as the argument.
    var O = Object(this);

    // 2. Let lenValue be the result of calling the Get internal
    //    method of O with the argument "length".
    // 3. Let len be ToUint32(lenValue).
    var len = O.length >>> 0;

    // 4. If IsCallable(callback) is false, throw a TypeError exception.
    // See: http://es5.github.com/#x9.11
    if (typeof callback !== 'function') {
      throw new TypeError(callback + ' is not a function');
    }

    // 5. If thisArg was supplied, let T be thisArg; else let T be undefined.
    if (arguments.length > 1) {
      T = thisArg;
    }

    // 6. Let A be a new array created as if by the expression new Array(len)
    //    where Array is the standard built-in constructor with that name and
    //    len is the value of len.
    A = new Array(len);

    // 7. Let k be 0
    k = 0;

    // 8. Repeat, while k < len
    while (k < len) {

      var kValue, mappedValue;

      // a. Let Pk be ToString(k).
      //   This is implicit for LHS operands of the in operator
      // b. Let kPresent be the result of calling the HasProperty internal
      //    method of O with argument Pk.
      //   This step can be combined with c
      // c. If kPresent is true, then
      if (k in O) {

        // i. Let kValue be the result of calling the Get internal
        //    method of O with argument Pk.
        kValue = O[k];

        // ii. Let mappedValue be the result of calling the Call internal
        //     method of callback with T as the this value and argument
        //     list containing kValue, k, and O.
        mappedValue = callback.call(T, kValue, k, O);

        // iii. Call the DefineOwnProperty internal method of A with arguments
        // Pk, Property Descriptor
        // { Value: mappedValue,
        //   Writable: true,
        //   Enumerable: true,
        //   Configurable: true },
        // and false.

        // In browsers that support Object.defineProperty, use the following:
        // Object.defineProperty(A, k, {
        //   value: mappedValue,
        //   writable: true,
        //   enumerable: true,
        //   configurable: true
        // });

        // For best browser support, use the following:
        A[k] = mappedValue;
      }
      // d. Increase k by 1.
      k++;
    }

    // 9. return A
    return A;
  };
}

var PagedTable = function (pagedTable) {
  var me = this;

  var source = function(pagedTable) {
    var sourceElems = [].slice.call(pagedTable.children).filter(function(e) {
      return e.hasAttribute("data-pagedtable-source");
    });

    if (sourceElems === null || sourceElems.length !== 1) {
      throw("A single data-pagedtable-source was not found");
    }

    return JSON.parse(sourceElems[0].innerHTML);
  }(pagedTable);

  var options = function(source) {
    var options = typeof(source.options) !== "undefined" &&
      source.options !== null ? source.options : {};

    var columns = typeof(options.columns) !== "undefined" ? options.columns : {};
    var rows = typeof(options.rows) !== "undefined" ? options.rows : {};

    var positiveIntOrNull = function(value) {
      return parseInt(value) >= 0 ? parseInt(value) : null;
    };

    return {
      pages: positiveIntOrNull(options.pages),
      rows: {
        min: positiveIntOrNull(rows.min),
        max: positiveIntOrNull(rows.max),
        total: positiveIntOrNull(rows.total)
      },
      columns: {
        min: positiveIntOrNull(columns.min),
        max: positiveIntOrNull(columns.max),
        total: positiveIntOrNull(columns.total)
      }
    };
  }(source);

  var Measurer = function() {

    // set some default initial values that will get adjusted in runtime
    me.measures = {
      padding: 12,
      character: 8,
      height: 15,
      defaults: true
    };

    me.calculate = function(measuresCell) {
      if (!me.measures.defaults)
        return;

      var measuresCellStyle = window.getComputedStyle(measuresCell, null);

      var newPadding = parsePadding(measuresCellStyle.paddingLeft) +
            parsePadding(measuresCellStyle.paddingRight);

      var sampleString = "ABCDEFGHIJ0123456789";
      var newCharacter = Math.ceil(measuresCell.clientWidth / sampleString.length);

      if (newPadding <= 0 || newCharacter <= 0)
        return;

      me.measures.padding = newPadding;
      me.measures.character = newCharacter;
      me.measures.height = measuresCell.clientHeight;
      me.measures.defaults = false;
    };

    return me;
  };

  var Page = function(data, options) {
    var me = this;

    var defaults = {
      max: 7,
      rows: 10
    };

    var totalPages = function() {
      return Math.ceil(data.length / me.rows);
    };

    me.number = 0;
    me.max = options.pages !== null ? options.pages : defaults.max;
    me.visible = me.max;
    me.rows = options.rows.min !== null ? options.rows.min : defaults.rows;
    me.total = totalPages();

    me.setRows = function(newRows) {
      me.rows = newRows;
      me.total = totalPages();
    };

    me.setPageNumber = function(newPageNumber) {
      if (newPageNumber < 0) newPageNumber = 0;
      if (newPageNumber >= me.total) newPageNumber = me.total - 1;

      me.number = newPageNumber;
    };

    me.setVisiblePages = function(visiblePages) {
      me.visible = Math.min(me.max, visiblePages);
      me.setPageNumber(me.number);
    };

    me.getVisiblePageRange = function() {
      var start = me.number - Math.max(Math.floor((me.visible - 1) / 2), 0);
      var end = me.number + Math.floor(me.visible / 2) + 1;
      var pageCount = me.total;

      if (start < 0) {
        var diffToStart = 0 - start;
        start += diffToStart;
        end += diffToStart;
      }

      if (end > pageCount) {
        var diffToEnd = end - pageCount;
        start -= diffToEnd;
        end -= diffToEnd;
      }

      start = start < 0 ? 0 : start;
      end = end >= pageCount ? pageCount : end;

      var first = false;
      var last = false;

      if (start > 0 && me.visible > 1) {
        start = start + 1;
        first = true;
      }

      if (end < pageCount && me.visible > 2) {
        end = end - 1;
        last = true;
      }

      return {
        first: first,
        start: start,
        end: end,
        last: last
      };
    };

    me.getRowStart = function() {
      var rowStart = page.number * page.rows;
      if (rowStart < 0)
        rowStart = 0;

      return rowStart;
    };

    me.getRowEnd = function() {
      var rowStart = me.getRowStart();
      return Math.min(rowStart + me.rows, data.length);
    };

    me.getPaddingRows = function() {
      var rowStart = me.getRowStart();
      var rowEnd = me.getRowEnd();
      return data.length > me.rows ? me.rows - (rowEnd - rowStart) : 0;
    };
  };

  var Columns = function(data, columns, options) {
    var me = this;

    me.defaults = {
      min: 5
    };

    me.number = 0;
    me.visible = 0;
    me.total = columns.length;
    me.subset = [];
    me.padding = 0;
    me.min = options.columns.min !== null ? options.columns.min : me.defaults.min;
    me.max = options.columns.max !== null ? options.columns.max : null;
    me.widths = {};

    var widthsLookAhead = Math.max(100, options.rows.min);
    var paddingColChars = 10;

    me.emptyNames = function() {
      columns.forEach(function(column) {
        if (columns.label !== null && columns.label !== "")
          return false;
      });

      return true;
    };

    var parsePadding = function(value) {
      return parseInt(value) >= 0 ? parseInt(value) : 0;
    };

    me.calculateWidths = function(measures) {
      columns.forEach(function(column) {
        var maxChars = Math.max(
          column.label.toString().length,
          column.type.toString().length
        );

        for (var idxRow = 0; idxRow < Math.min(widthsLookAhead, data.length); idxRow++) {
          maxChars = Math.max(maxChars, data[idxRow][column.name.toString()].length);
        }

        me.widths[column.name] = {
          // width in characters
          chars: maxChars,
          // width for the inner html columns
          inner: maxChars * measures.character,
          // width adding outer styles like padding
          outer: maxChars * measures.character + measures.padding
        };
      });
    };

    me.getWidth = function() {
      var widthOuter = 0;
      for (var idxCol = 0; idxCol < me.subset.length; idxCol++) {
        var columnName = me.subset[idxCol].name;
        widthOuter = widthOuter + me.widths[columnName].outer;
      }

      widthOuter = widthOuter + me.padding * paddingColChars * measurer.measures.character;

      if (me.hasMoreLeftColumns()) {
        widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding;
      }

      if (me.hasMoreRightColumns()) {
        widthOuter = widthOuter + columnNavigationWidthPX + measurer.measures.padding;
      }

      return widthOuter;
    };

    me.updateSlice = function() {
      if (me.number + me.visible >= me.total)
        me.number = me.total - me.visible;

      if (me.number < 0) me.number = 0;

      me.subset = columns.slice(me.number, Math.min(me.number + me.visible, me.total));

      me.subset = me.subset.map(function(column) {
        Object.keys(column).forEach(function(colKey) {
          column[colKey] = column[colKey] === null ? "" : column[colKey].toString();
        });

        column.width = null;
        return column;
      });
    };

    me.setVisibleColumns = function(columnNumber, newVisibleColumns, paddingCount) {
      me.number = columnNumber;
      me.visible = newVisibleColumns;
      me.padding = paddingCount;

      me.updateSlice();
    };

    me.incColumnNumber = function(increment) {
      me.number = me.number + increment;
    };

    me.setColumnNumber = function(newNumber) {
      me.number = newNumber;
    };

    me.setPaddingCount = function(newPadding) {
      me.padding = newPadding;
    };

    me.getPaddingCount = function() {
      return me.padding;
    };

    me.hasMoreLeftColumns = function() {
      return me.number > 0;
    };

    me.hasMoreRightColumns = function() {
      return me.number + me.visible < me.total;
    };

    me.updateSlice(0);
    return me;
  };

  var data = source.data;
  var page = new Page(data, options);
  var measurer = new Measurer(data, options);
  var columns = new Columns(data, source.columns, options);

  var table = null;
  var tableDiv = null;
  var header = null;
  var footer = null;
  var tbody = null;

  // Caches pagedTable.clientWidth, specially for webkit
  var cachedPagedTableClientWidth = null;

  var onChangeCallbacks = [];

  var clearSelection = function() {
    if(document.selection && document.selection.empty) {
      document.selection.empty();
    } else if(window.getSelection) {
      var sel = window.getSelection();
      sel.removeAllRanges();
    }
  };

  var columnNavigationWidthPX = 5;

  var renderColumnNavigation = function(increment, backwards) {
    var arrow = document.createElement("div");
    arrow.setAttribute("style",
      "border-top: " + columnNavigationWidthPX + "px solid transparent;" +
      "border-bottom: " + columnNavigationWidthPX + "px solid transparent;" +
      "border-" + (backwards ? "right" : "left") + ": " + columnNavigationWidthPX + "px solid;");

    var header = document.createElement("th");
    header.appendChild(arrow);
    header.setAttribute("style",
      "cursor: pointer;" +
      "vertical-align: middle;" +
      "min-width: " + columnNavigationWidthPX + "px;" +
      "width: " + columnNavigationWidthPX + "px;");

    header.onclick = function() {
      columns.incColumnNumber(backwards ? -1 : increment);

      me.animateColumns(backwards);
      renderFooter();

      clearSelection();
      triggerOnChange();
    };

    return header;
  };

  var maxColumnWidth = function(width) {
    var padding = 80;
    var columnMax = Math.max(cachedPagedTableClientWidth - padding, 0);

    return parseInt(width) > 0 ?
      Math.min(columnMax, parseInt(width)) + "px" :
      columnMax + "px";
  };

  var clearHeader = function() {
    var thead = pagedTable.querySelectorAll("thead")[0];
    thead.innerHTML = "";
  };

  var renderHeader = function(clear) {
    cachedPagedTableClientWidth = pagedTable.clientWidth;

    var fragment = document.createDocumentFragment();

    header = document.createElement("tr");
    fragment.appendChild(header);

    if (columns.number > 0)
      header.appendChild(renderColumnNavigation(-columns.visible, true));

    columns.subset = columns.subset.map(function(columnData) {
      var column = document.createElement("th");
      column.setAttribute("align", columnData.align);
      column.style.textAlign = columnData.align;

      column.style.maxWidth = maxColumnWidth(null);
      if (columnData.width) {
        column.style.minWidth =
          column.style.maxWidth = maxColumnWidth(columnData.width);
      }

      var columnName = document.createElement("div");
      columnName.setAttribute("class", "pagedtable-header-name");
      if (columnData.label === "") {
        columnName.innerHTML = "&nbsp;";
      }
      else {
        columnName.appendChild(document.createTextNode(columnData.label));
      }
      column.appendChild(columnName);

      var columnType = document.createElement("div");
      columnType.setAttribute("class", "pagedtable-header-type");
      if (columnData.type === "") {
        columnType.innerHTML = "&nbsp;";
      }
      else {
        columnType.appendChild(document.createTextNode("<" + columnData.type + ">"));
      }
      column.appendChild(columnType);

      header.appendChild(column);

      columnData.element = column;

      return columnData;
    });

    for (var idx = 0; idx < columns.getPaddingCount(); idx++) {
      var paddingCol = document.createElement("th");
      paddingCol.setAttribute("class", "pagedtable-padding-col");
      header.appendChild(paddingCol);
    }

    if (columns.number + columns.visible < columns.total)
      header.appendChild(renderColumnNavigation(columns.visible, false));

    if (typeof(clear) == "undefined" || clear) clearHeader();
    var thead = pagedTable.querySelectorAll("thead")[0];
    thead.appendChild(fragment);
  };

  me.animateColumns = function(backwards) {
    var thead = pagedTable.querySelectorAll("thead")[0];

    var headerOld = thead.querySelectorAll("tr")[0];
    var tbodyOld = table.querySelectorAll("tbody")[0];

    me.fitColumns(backwards);

    renderHeader(false);

    header.style.opacity = "0";
    header.style.transform = backwards ? "translateX(-30px)" : "translateX(30px)";
    header.style.transition = "transform 200ms linear, opacity 200ms";
    header.style.transitionDelay = "0";

    renderBody(false);

    if (headerOld) {
      headerOld.style.position = "absolute";
      headerOld.style.transform = "translateX(0px)";
      headerOld.style.opacity = "1";
      headerOld.style.transition = "transform 100ms linear, opacity 100ms";
      headerOld.setAttribute("class", "pagedtable-remove-head");
      if (headerOld.style.transitionEnd) {
        headerOld.addEventListener("transitionend", function() {
          var headerOldByClass = thead.querySelector(".pagedtable-remove-head");
          if (headerOldByClass) thead.removeChild(headerOldByClass);
        });
      }
      else {
        thead.removeChild(headerOld);
      }
    }

    if (tbodyOld) table.removeChild(tbodyOld);

    tbody.style.opacity = "0";
    tbody.style.transition = "transform 200ms linear, opacity 200ms";
    tbody.style.transitionDelay = "0ms";

    // force relayout
    window.getComputedStyle(header).opacity;
    window.getComputedStyle(tbody).opacity;

    if (headerOld) {
      headerOld.style.transform = backwards ? "translateX(20px)" : "translateX(-30px)";
      headerOld.style.opacity = "0";
    }

    header.style.transform = "translateX(0px)";
    header.style.opacity = "1";

    tbody.style.opacity = "1";
  }

  me.onChange = function(callback) {
    onChangeCallbacks.push(callback);
  };

  var triggerOnChange = function() {
    onChangeCallbacks.forEach(function(onChange) {
      onChange();
    });
  };

  var clearBody = function() {
    if (tbody) {
      table.removeChild(tbody);
      tbody = null;
    }
  };

  var renderBody = function(clear) {
    cachedPagedTableClientWidth = pagedTable.clientWidth

    var fragment = document.createDocumentFragment();

    var pageData = data.slice(page.getRowStart(), page.getRowEnd());

    pageData.forEach(function(dataRow, idxRow) {
      var htmlRow = document.createElement("tr");
      htmlRow.setAttribute("class", (idxRow % 2 !==0) ? "even" : "odd");

      if (columns.hasMoreLeftColumns())
        htmlRow.appendChild(document.createElement("td"));

      columns.subset.forEach(function(columnData) {
        var cellName = columnData.name;
        var dataCell = dataRow[cellName];
        var htmlCell = document.createElement("td");

        if (dataCell === "NA") htmlCell.setAttribute("class", "pagedtable-na-cell");
        if (dataCell === "__NA__") dataCell = "NA";

        var cellText = document.createTextNode(dataCell);
        htmlCell.appendChild(cellText);
        if (dataCell.length > 50) {
          htmlCell.setAttribute("title", dataCell);
        }
        htmlCell.setAttribute("align", columnData.align);
        htmlCell.style.textAlign = columnData.align;
        htmlCell.style.maxWidth = maxColumnWidth(null);
        if (columnData.width) {
          htmlCell.style.minWidth = htmlCell.style.maxWidth = maxColumnWidth(columnData.width);
        }
        htmlRow.appendChild(htmlCell);
      });

      for (var idx = 0; idx < columns.getPaddingCount(); idx++) {
        var paddingCol = document.createElement("td");
        paddingCol.setAttribute("class", "pagedtable-padding-col");
        htmlRow.appendChild(paddingCol);
      }

      if (columns.hasMoreRightColumns())
        htmlRow.appendChild(document.createElement("td"));

      fragment.appendChild(htmlRow);
    });

    for (var idxPadding = 0; idxPadding < page.getPaddingRows(); idxPadding++) {
      var paddingRow = document.createElement("tr");

      var paddingCellRow = document.createElement("td");
      paddingCellRow.innerHTML = "&nbsp;";
      paddingCellRow.setAttribute("colspan", "100%");
      paddingRow.appendChild(paddingCellRow);

      fragment.appendChild(paddingRow);
    }

    if (typeof(clear) == "undefined" || clear) clearBody();
    tbody = document.createElement("tbody");
    tbody.appendChild(fragment);

    table.appendChild(tbody);
  };

  var getLabelInfo = function() {
    var pageStart = page.getRowStart();
    var pageEnd = page.getRowEnd();
    var totalRows = data.length;

    var totalRowsLabel = options.rows.total ? options.rows.total : totalRows;
    var totalRowsLabelFormat = totalRowsLabel.toString().replace(/(\d)(?=(\d\d\d)+(?!\d))/g, '$1,');

    var infoText = (pageStart + 1) + "-" + pageEnd + " of " + totalRowsLabelFormat + " rows";
    if (totalRows < page.rows) {
      infoText = totalRowsLabel + " row" + (totalRows != 1 ? "s" : "");
    }
    if (columns.total > columns.visible) {
      var totalColumnsLabel = options.columns.total ? options.columns.total : columns.total;

      infoText = infoText + " | " + (columns.number + 1) + "-" +
        (Math.min(columns.number + columns.visible, columns.total)) +
        " of " + totalColumnsLabel + " columns";
    }

    return infoText;
  };

  var clearFooter = function() {
    footer = pagedTable.querySelectorAll("div.pagedtable-footer")[0];
    footer.innerHTML = "";

    return footer;
  };

  var createPageLink = function(idxPage) {
    var pageLink = document.createElement("a");
    pageLinkClass = idxPage === page.number ? "pagedtable-index pagedtable-index-current" : "pagedtable-index";
    pageLink.setAttribute("class", pageLinkClass);
    pageLink.setAttribute("data-page-index", idxPage);
    pageLink.onclick = function() {
      page.setPageNumber(parseInt(this.getAttribute("data-page-index")));
      renderBody();
      renderFooter();

      triggerOnChange();
    };

    pageLink.appendChild(document.createTextNode(idxPage + 1));

    return pageLink;
  }

  var renderFooter = function() {
    footer = clearFooter();

    var next = document.createElement("a");
    next.appendChild(document.createTextNode("Next"));
    next.onclick = function() {
      page.setPageNumber(page.number + 1);
      renderBody();
      renderFooter();

      triggerOnChange();
    };
    if (data.length > page.rows) footer.appendChild(next);

    var pageNumbers = document.createElement("div");
    pageNumbers.setAttribute("class", "pagedtable-indexes");

    var pageRange = page.getVisiblePageRange();

    if (pageRange.first) {
      var pageLink = createPageLink(0);
      pageNumbers.appendChild(pageLink);

      var pageSeparator = document.createElement("div");
      pageSeparator.setAttribute("class", "pagedtable-index-separator-left");
      pageSeparator.appendChild(document.createTextNode("..."))
      pageNumbers.appendChild(pageSeparator);
    }

    for (var idxPage = pageRange.start; idxPage < pageRange.end; idxPage++) {
      var pageLink = createPageLink(idxPage);

      pageNumbers.appendChild(pageLink);
    }

    if (pageRange.last) {
      var pageSeparator = document.createElement("div");
      pageSeparator.setAttribute("class", "pagedtable-index-separator-right");
      pageSeparator.appendChild(document.createTextNode("..."))
      pageNumbers.appendChild(pageSeparator);

      var pageLink = createPageLink(page.total - 1);
      pageNumbers.appendChild(pageLink);
    }

    if (data.length > page.rows) footer.appendChild(pageNumbers);

    var previous = document.createElement("a");
    previous.appendChild(document.createTextNode("Previous"));
    previous.onclick = function() {
      page.setPageNumber(page.number - 1);
      renderBody();
      renderFooter();

      triggerOnChange();
    };
    if (data.length > page.rows) footer.appendChild(previous);

    var infoLabel = document.createElement("div");
    infoLabel.setAttribute("class", "pagedtable-info");
    infoLabel.setAttribute("title", getLabelInfo());
    infoLabel.appendChild(document.createTextNode(getLabelInfo()));
    footer.appendChild(infoLabel);

    var enabledClass = "pagedtable-index-nav";
    var disabledClass = "pagedtable-index-nav pagedtable-index-nav-disabled";
    previous.setAttribute("class", page.number <= 0 ? disabledClass : enabledClass);
    next.setAttribute("class", (page.number + 1) * page.rows >= data.length ? disabledClass : enabledClass);
  };

  var measuresCell = null;

  var renderMeasures = function() {
    var measuresTable = document.createElement("table");
    measuresTable.style.visibility = "hidden";
    measuresTable.style.position = "absolute";
    measuresTable.style.whiteSpace = "nowrap";
    measuresTable.style.height = "auto";
    measuresTable.style.width = "auto";

    var measuresRow = document.createElement("tr");
    measuresTable.appendChild(measuresRow);

    measuresCell = document.createElement("td");
    var sampleString = "ABCDEFGHIJ0123456789";
    measuresCell.appendChild(document.createTextNode(sampleString));

    measuresRow.appendChild(measuresCell);

    tableDiv.appendChild(measuresTable);
  }

  me.init = function() {
    tableDiv = document.createElement("div");
    pagedTable.appendChild(tableDiv);
    var pagedTableClass = data.length > 0 ?
      "pagedtable pagedtable-not-empty" :
      "pagedtable pagedtable-empty";

    if (columns.total == 0 || (columns.emptyNames() && data.length == 0)) {
      pagedTableClass = pagedTableClass + " pagedtable-empty-columns";
    }

    tableDiv.setAttribute("class", pagedTableClass);

    renderMeasures();
    measurer.calculate(measuresCell);
    columns.calculateWidths(measurer.measures);

    table = document.createElement("table");
    table.setAttribute("cellspacing", "0");
    table.setAttribute("class", "table table-condensed");
    tableDiv.appendChild(table);

    table.appendChild(document.createElement("thead"));

    var footerDiv = document.createElement("div");
    footerDiv.setAttribute("class", "pagedtable-footer");
    tableDiv.appendChild(footerDiv);

    // if the host has not yet provided horizontal space, render hidden
    if (tableDiv.clientWidth <= 0) {
      tableDiv.style.opacity = "0";
    }

    me.render();

    // retry seizing columns later if the host has not provided space
    function retryFit() {
      if (tableDiv.clientWidth <= 0) {
        setTimeout(retryFit, 100);
      } else {
        me.render();
        triggerOnChange();
      }
    }
    if (tableDiv.clientWidth <= 0) {
      retryFit();
    }
  };

  var registerWidths = function() {
    columns.subset = columns.subset.map(function(column) {
      column.width = columns.widths[column.name].inner;
      return column;
    });
  };

  var parsePadding = function(value) {
    return parseInt(value) >= 0 ? parseInt(value) : 0;
  };

  me.fixedHeight = function() {
    return options.rows.max != null;
  }

  me.fitRows = function() {
    if (me.fixedHeight())
      return;

    measurer.calculate(measuresCell);

    var rows = options.rows.min !== null ? options.rows.min : 0;
    var headerHeight = header !== null && header.offsetHeight > 0 ? header.offsetHeight : 0;
    var footerHeight = footer !== null && footer.offsetHeight > 0 ? footer.offsetHeight : 0;

    if (pagedTable.offsetHeight > 0) {
      var availableHeight = pagedTable.offsetHeight - headerHeight - footerHeight;
      rows = Math.floor((availableHeight) / measurer.measures.height);
    }

    rows = options.rows.min !== null ? Math.max(options.rows.min, rows) : rows;

    page.setRows(rows);
  }

  // The goal of this function is to add as many columns as possible
  // starting from left-to-right, when the right most limit is reached
  // it tries to add columns from the left as well.
  //
  // When startBackwards is true columns are added from right-to-left
  me.fitColumns = function(startBackwards) {
    measurer.calculate(measuresCell);
    columns.calculateWidths(measurer.measures);

    if (tableDiv.clientWidth > 0) {
      tableDiv.style.opacity = 1;
    }

    var visibleColumns = tableDiv.clientWidth <= 0 ? Math.max(columns.min, 1) : 1;
    var columnNumber = columns.number;
    var paddingCount = 0;

    // track a list of added columns as we build the visible ones to allow us
    // to remove columns when they don't fit anymore.
    var columnHistory = [];

    var lastTableHeight = 0;
    var backwards = startBackwards;

    var tableDivStyle = window.getComputedStyle(tableDiv, null);
    var tableDivPadding = parsePadding(tableDivStyle.paddingLeft) +
      parsePadding(tableDivStyle.paddingRight);

    var addPaddingCol = false;
    var currentWidth = 0;

    while (true) {
      columns.setVisibleColumns(columnNumber, visibleColumns, paddingCount);
      currentWidth = columns.getWidth();

      if (tableDiv.clientWidth - tableDivPadding < currentWidth) {
        break;
      }

      columnHistory.push({
        columnNumber: columnNumber,
        visibleColumns: visibleColumns,
        paddingCount: paddingCount
      });

      if (columnHistory.length > 100) {
        console.error("More than 100 tries to fit columns, aborting");
        break;
      }

      if (columns.max !== null &&
        columns.visible + columns.getPaddingCount() >= columns.max) {
        break;
      }

      // if we run out of right-columns
      if (!backwards && columnNumber + columns.visible >= columns.total) {
        // if we started adding right-columns, try adding left-columns
        if (!startBackwards && columnNumber > 0) {
          backwards = true;
        }
        else if (columns.min === null || visibleColumns + columns.getPaddingCount() >= columns.min) {
          break;
        }
        else {
          paddingCount = paddingCount + 1;
        }
      }

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<h1 class="title toc-ignore">Replication Codes for ‘From Olympic Gold to
Government Approval?: Limited Evidence of Attribution Error from the
2024 Paris Games’</h1>
<h4 class="author">Hanako Ohmura, Masahiro Zenkyo, Masaki Hata, and
Tetsuya Matsubayashi</h4>
<h4 class="date">2026-01-26</h4>

</div>


<div id="replication-materials" class="section level1 unnumbered">
<h1 class="unnumbered">Replication Materials</h1>
<div class="alertbox">
<p>This repository provides the data and code required to reproduce the
analyses and figures reported in “From Olympic Gold to Government
Approval? Limited Evidence of Attribution Error from the 2024 Paris
Games” in <em>Japanese Journal of Political Science</em>. All scripts
are written in R and are organized to mirror the structure of the paper
and Supplementary Information.</p>
</div>
</div>
<div id="required-r-packages" class="section level1 unnumbered">
<h1 class="unnumbered">Required R Packages</h1>
<p>We conducted the analyses in R (Version 4.4.2). The packages listed
below are needed to reproduce the results. You can install any missing
packages with the code snippet provided. We recommend using renv to
snapshot and restore the exact package versions.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="fu">library</span>(cobalt) <span class="co"># Ver.4.5.5</span></span>
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a><span class="fu">library</span>(ebal) <span class="co">#Ver.0.1-8</span></span>
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a><span class="fu">library</span>(equivalence) <span class="co">#Ver.0.8.2</span></span>
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="fu">library</span>(fracdiff) <span class="co">#Ver.1.5-3</span></span>
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a><span class="fu">library</span>(ggsignif) <span class="co">#Ver.0.6.4</span></span>
<span id="cb1-6"><a href="#cb1-6" tabindex="-1"></a><span class="fu">library</span>(ggthemes) <span class="do">##Ver.5.1.0</span></span>
<span id="cb1-7"><a href="#cb1-7" tabindex="-1"></a><span class="fu">library</span>(grid) <span class="co">#Ver.2.3</span></span>
<span id="cb1-8"><a href="#cb1-8" tabindex="-1"></a><span class="fu">library</span>(gridExtra) <span class="co">#Ver.2.3</span></span>
<span id="cb1-9"><a href="#cb1-9" tabindex="-1"></a><span class="fu">library</span>(grf) <span class="co">#Ver.2.4.0</span></span>
<span id="cb1-10"><a href="#cb1-10" tabindex="-1"></a><span class="fu">library</span>(lmtest) <span class="co">#Ver.0.9-40</span></span>
<span id="cb1-11"><a href="#cb1-11" tabindex="-1"></a><span class="fu">library</span>(lubridate) <span class="co">#Ver.1.9.4</span></span>
<span id="cb1-12"><a href="#cb1-12" tabindex="-1"></a><span class="fu">library</span>(psych) <span class="co">#Ver.2.4.12</span></span>
<span id="cb1-13"><a href="#cb1-13" tabindex="-1"></a><span class="fu">library</span>(rddensity) <span class="co">#Ver.2.6</span></span>
<span id="cb1-14"><a href="#cb1-14" tabindex="-1"></a><span class="fu">library</span>(rdrobust) <span class="co">#Ver.2.0.0</span></span>
<span id="cb1-15"><a href="#cb1-15" tabindex="-1"></a><span class="fu">library</span>(readr) <span class="co">#Ver.2.1.5</span></span>
<span id="cb1-16"><a href="#cb1-16" tabindex="-1"></a><span class="fu">library</span>(sandwich) <span class="co">#Ver.3.1-1</span></span>
<span id="cb1-17"><a href="#cb1-17" tabindex="-1"></a><span class="fu">library</span>(scales) <span class="co">#Ver.1.3.0</span></span>
<span id="cb1-18"><a href="#cb1-18" tabindex="-1"></a><span class="fu">library</span>(stargazer) <span class="co">#Ver.5.2.3</span></span>
<span id="cb1-19"><a href="#cb1-19" tabindex="-1"></a><span class="fu">library</span>(tibble) <span class="co">#Ver.3.3.0</span></span>
<span id="cb1-20"><a href="#cb1-20" tabindex="-1"></a><span class="fu">library</span>(tidyverse) <span class="co">#Ver.2.0.0</span></span>
<span id="cb1-21"><a href="#cb1-21" tabindex="-1"></a><span class="fu">library</span>(tseries) <span class="co">#Ver.0.10-58</span></span>
<span id="cb1-22"><a href="#cb1-22" tabindex="-1"></a><span class="fu">library</span>(vtable) <span class="co">#Ver.1.4.8</span></span>
<span id="cb1-23"><a href="#cb1-23" tabindex="-1"></a><span class="fu">library</span>(WeightIt) <span class="co">#Ver.1.4.0</span></span></code></pre></div>
</div>
<div id="setting-variables" class="section level1 unnumbered">
<h1 class="unnumbered">Setting Variables</h1>
<div id="setting-variables-for-the-first-gold-medal" class="section level2 unnumbered">
<h2 class="unnumbered">Setting variables for the first gold medal</h2>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">1234</span>)</span>
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a>cutoff_time <span class="ot">&lt;-</span> <span class="fu">as.POSIXct</span>(<span class="st">&quot;2024-07-28 01:01:00&quot;</span>, <span class="at">tz =</span> <span class="st">&quot;Asia/Tokyo&quot;</span>)</span>
<span id="cb2-3"><a href="#cb2-3" tabindex="-1"></a></span>
<span id="cb2-4"><a href="#cb2-4" tabindex="-1"></a>judo48p <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="st">&quot;judo48pure.csv&quot;</span>, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">|&gt;</span></span>
<span id="cb2-5"><a href="#cb2-5" tabindex="-1"></a>  <span class="fu">mutate</span>( </span>
<span id="cb2-6"><a href="#cb2-6" tabindex="-1"></a>    <span class="at">StartDate =</span> <span class="fu">force_tz</span>(</span>
<span id="cb2-7"><a href="#cb2-7" tabindex="-1"></a>      <span class="fu">as.POSIXct</span>(StartDate, <span class="at">format =</span> <span class="st">&quot;%Y/%m/%d %H:%M&quot;</span>, <span class="at">tz =</span> <span class="st">&quot;America/Denver&quot;</span>),</span>
<span id="cb2-8"><a href="#cb2-8" tabindex="-1"></a>      <span class="at">tzone =</span> <span class="st">&quot;America/Denver&quot;</span></span>
<span id="cb2-9"><a href="#cb2-9" tabindex="-1"></a>    ) <span class="sc">|&gt;</span> <span class="fu">with_tz</span>(<span class="st">&quot;Asia/Tokyo&quot;</span>), <span class="co"># setting cutoff</span></span>
<span id="cb2-10"><a href="#cb2-10" tabindex="-1"></a>    </span>
<span id="cb2-11"><a href="#cb2-11" tabindex="-1"></a>    </span>
<span id="cb2-12"><a href="#cb2-12" tabindex="-1"></a>    <span class="at">cutoff_time =</span> .env<span class="sc">$</span>cutoff_time,</span>
<span id="cb2-13"><a href="#cb2-13" tabindex="-1"></a>    </span>
<span id="cb2-14"><a href="#cb2-14" tabindex="-1"></a>    <span class="at">cabap   =</span> <span class="fu">case_when</span>(Q1 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">1</span>,  <span class="co"># cabinet approval</span></span>
<span id="cb2-15"><a href="#cb2-15" tabindex="-1"></a>                        Q1 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb2-16"><a href="#cb2-16" tabindex="-1"></a>                        Q1 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="cn">NA_real_</span>,</span>
<span id="cb2-17"><a href="#cb2-17" tabindex="-1"></a>                        <span class="cn">TRUE</span>    <span class="sc">~</span> <span class="cn">NA_real_</span>),</span>
<span id="cb2-18"><a href="#cb2-18" tabindex="-1"></a>    <span class="at">exposure =</span> <span class="fu">case_when</span>(Q6 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">1</span>, <span class="co">#for manipulation check</span></span>
<span id="cb2-19"><a href="#cb2-19" tabindex="-1"></a>                         Q6 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">6</span>,<span class="dv">7</span>,<span class="dv">8</span>) <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb2-20"><a href="#cb2-20" tabindex="-1"></a>                         <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">NA_real_</span>),</span>
<span id="cb2-21"><a href="#cb2-21" tabindex="-1"></a>    </span>
<span id="cb2-22"><a href="#cb2-22" tabindex="-1"></a>    <span class="at">ftj  =</span> Q3_11,</span>
<span id="cb2-23"><a href="#cb2-23" tabindex="-1"></a>    <span class="at">ftl  =</span> Q3_1,</span>
<span id="cb2-24"><a href="#cb2-24" tabindex="-1"></a>    <span class="at">ftki =</span> Q3_6,</span>
<span id="cb2-25"><a href="#cb2-25" tabindex="-1"></a>    </span>
<span id="cb2-26"><a href="#cb2-26" tabindex="-1"></a>    <span class="at">busi =</span> <span class="fu">case_when</span>(Q8_1 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">5</span>, <span class="co">#economic evaluation: business conditions</span></span>
<span id="cb2-27"><a href="#cb2-27" tabindex="-1"></a>                     Q8_1 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">4</span>,</span>
<span id="cb2-28"><a href="#cb2-28" tabindex="-1"></a>                     Q8_1 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="dv">2</span>,</span>
<span id="cb2-29"><a href="#cb2-29" tabindex="-1"></a>                     Q8_1 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb2-30"><a href="#cb2-30" tabindex="-1"></a>                     <span class="cn">TRUE</span>      <span class="sc">~</span> <span class="dv">3</span>),</span>
<span id="cb2-31"><a href="#cb2-31" tabindex="-1"></a>    <span class="at">liv  =</span> <span class="fu">case_when</span>(Q7_2 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">5</span>, <span class="co">#economic evaluation: pocketbook</span></span>
<span id="cb2-32"><a href="#cb2-32" tabindex="-1"></a>                     Q7_2 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">4</span>,</span>
<span id="cb2-33"><a href="#cb2-33" tabindex="-1"></a>                     Q7_2 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="dv">2</span>,</span>
<span id="cb2-34"><a href="#cb2-34" tabindex="-1"></a>                     Q7_2 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb2-35"><a href="#cb2-35" tabindex="-1"></a>                     <span class="cn">TRUE</span>      <span class="sc">~</span> <span class="dv">3</span>),</span>
<span id="cb2-36"><a href="#cb2-36" tabindex="-1"></a>    </span>
<span id="cb2-37"><a href="#cb2-37" tabindex="-1"></a>    <span class="at">psu_rul =</span> <span class="fu">case_when</span>(Q2 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">14</span>,<span class="dv">15</span>) <span class="sc">~</span> <span class="cn">NA_real_</span>, <span class="co"># in-parisans</span></span>
<span id="cb2-38"><a href="#cb2-38" tabindex="-1"></a>                        Q2 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">4</span>)    <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb2-39"><a href="#cb2-39" tabindex="-1"></a>                        <span class="cn">TRUE</span>              <span class="sc">~</span> <span class="dv">0</span>),</span>
<span id="cb2-40"><a href="#cb2-40" tabindex="-1"></a>    </span>
<span id="cb2-41"><a href="#cb2-41" tabindex="-1"></a>    <span class="at">age       =</span> Q11,</span>
<span id="cb2-42"><a href="#cb2-42" tabindex="-1"></a>    <span class="at">female    =</span> <span class="fu">case_when</span>(Q10 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb2-43"><a href="#cb2-43" tabindex="-1"></a>                          Q10 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb2-44"><a href="#cb2-44" tabindex="-1"></a>                          Q10 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="cn">NA_real_</span>,</span>
<span id="cb2-45"><a href="#cb2-45" tabindex="-1"></a>                          <span class="cn">TRUE</span>      <span class="sc">~</span> <span class="cn">NA_real_</span>),</span>
<span id="cb2-46"><a href="#cb2-46" tabindex="-1"></a>    <span class="at">education =</span> <span class="fu">na_if</span>(Q12, <span class="dv">5</span>),</span>
<span id="cb2-47"><a href="#cb2-47" tabindex="-1"></a>    <span class="at">income    =</span> <span class="fu">if_else</span>(Q13 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">15</span>,<span class="dv">16</span>), <span class="cn">NA</span>, Q13),</span>
<span id="cb2-48"><a href="#cb2-48" tabindex="-1"></a>    </span>
<span id="cb2-49"><a href="#cb2-49" tabindex="-1"></a>    <span class="fu">set.seed</span>(<span class="dv">1234</span>),</span>
<span id="cb2-50"><a href="#cb2-50" tabindex="-1"></a>    </span>
<span id="cb2-51"><a href="#cb2-51" tabindex="-1"></a>    <span class="at">cutoff      =</span> <span class="fu">as.integer</span>(StartDate <span class="sc">&gt;=</span> cutoff_time),</span>
<span id="cb2-52"><a href="#cb2-52" tabindex="-1"></a>    <span class="at">running_var =</span> <span class="fu">as.numeric</span>(<span class="fu">difftime</span>(StartDate, cutoff_time, <span class="at">units =</span> <span class="st">&quot;mins&quot;</span>)),</span>
<span id="cb2-53"><a href="#cb2-53" tabindex="-1"></a>    <span class="at">running_var2 =</span> running_var <span class="sc">+</span> <span class="fu">rnorm</span>(<span class="fu">length</span>(running_var), <span class="at">mean =</span> <span class="dv">0</span>, <span class="at">sd =</span> <span class="fl">0.01</span>)  <span class="co">#setting running variables</span></span>
<span id="cb2-54"><a href="#cb2-54" tabindex="-1"></a>  )</span>
<span id="cb2-55"><a href="#cb2-55" tabindex="-1"></a></span>
<span id="cb2-56"><a href="#cb2-56" tabindex="-1"></a></span>
<span id="cb2-57"><a href="#cb2-57" tabindex="-1"></a>fa_result <span class="ot">&lt;-</span> judo48p <span class="sc">|&gt;</span></span>
<span id="cb2-58"><a href="#cb2-58" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(Q7_1, Q7_2, Q7_3, Q7_4, Q7_5, Q7_6) <span class="sc">|&gt;</span></span>
<span id="cb2-59"><a href="#cb2-59" tabindex="-1"></a>  psych<span class="sc">::</span><span class="fu">fa</span>(<span class="at">nfactors =</span> <span class="dv">2</span>, <span class="at">rotate =</span> <span class="st">&quot;varimax&quot;</span>, <span class="at">scores =</span> <span class="st">&quot;regression&quot;</span>) <span class="co">#patriotism</span></span>
<span id="cb2-60"><a href="#cb2-60" tabindex="-1"></a></span>
<span id="cb2-61"><a href="#cb2-61" tabindex="-1"></a>judo48p <span class="ot">&lt;-</span> judo48p <span class="sc">|&gt;</span></span>
<span id="cb2-62"><a href="#cb2-62" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">patriotism =</span> <span class="fu">as.numeric</span>(fa_result<span class="sc">$</span>scores[, <span class="dv">1</span>]))</span>
<span id="cb2-63"><a href="#cb2-63" tabindex="-1"></a></span>
<span id="cb2-64"><a href="#cb2-64" tabindex="-1"></a>judo48p_patriot_hi <span class="ot">&lt;-</span> judo48p <span class="sc">|&gt;</span></span>
<span id="cb2-65"><a href="#cb2-65" tabindex="-1"></a>  <span class="fu">filter</span>(patriotism <span class="sc">&gt;=</span> <span class="fu">median</span>(patriotism, <span class="at">na.rm =</span> <span class="cn">TRUE</span>))</span>
<span id="cb2-66"><a href="#cb2-66" tabindex="-1"></a></span>
<span id="cb2-67"><a href="#cb2-67" tabindex="-1"></a></span>
<span id="cb2-68"><a href="#cb2-68" tabindex="-1"></a>df_rdd48 <span class="ot">&lt;-</span> judo48p <span class="sc">|&gt;</span></span>
<span id="cb2-69"><a href="#cb2-69" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(</span>
<span id="cb2-70"><a href="#cb2-70" tabindex="-1"></a>    StartDate, cabap, ftj, busi, liv, ftki, ftl,</span>
<span id="cb2-71"><a href="#cb2-71" tabindex="-1"></a>    cutoff, age, female, education, income, psu_rul,</span>
<span id="cb2-72"><a href="#cb2-72" tabindex="-1"></a>    exposure, cutoff_time,</span>
<span id="cb2-73"><a href="#cb2-73" tabindex="-1"></a>    <span class="at">X1 =</span> Q7_1, <span class="at">X2 =</span> Q7_2, <span class="at">X3 =</span> Q7_3, <span class="at">X4 =</span> Q7_4, <span class="at">X5 =</span> Q7_5, <span class="at">X6 =</span> Q7_6,</span>
<span id="cb2-74"><a href="#cb2-74" tabindex="-1"></a>    patriotism, running_var, running_var2</span>
<span id="cb2-75"><a href="#cb2-75" tabindex="-1"></a>  ) <span class="sc">|&gt;</span></span>
<span id="cb2-76"><a href="#cb2-76" tabindex="-1"></a>  tidyr<span class="sc">::</span><span class="fu">drop_na</span>() </span>
<span id="cb2-77"><a href="#cb2-77" tabindex="-1"></a></span>
<span id="cb2-78"><a href="#cb2-78" tabindex="-1"></a><span class="fu">dim</span>(df_rdd48)</span></code></pre></div>
<pre><code>## [1] 740  24</code></pre>
</div>
<div id="setting-variables-for-the-second-gold-medal" class="section level2 unnumbered">
<h2 class="unnumbered">Setting variables for the second gold medal</h2>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">1234</span>)</span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a></span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a>cutoff_time <span class="ot">&lt;-</span> <span class="fu">as.POSIXct</span>(<span class="st">&quot;2024-07-29 00:56:18&quot;</span>,</span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a>                          <span class="at">format =</span> <span class="st">&quot;%Y-%m-%d %H:%M:%S&quot;</span>, <span class="at">tz =</span> <span class="st">&quot;Asia/Tokyo&quot;</span>)</span>
<span id="cb4-5"><a href="#cb4-5" tabindex="-1"></a></span>
<span id="cb4-6"><a href="#cb4-6" tabindex="-1"></a></span>
<span id="cb4-7"><a href="#cb4-7" tabindex="-1"></a>judo66p <span class="ot">&lt;-</span> <span class="fu">read_csv</span>(<span class="st">&quot;judo52pure.csv&quot;</span>, <span class="at">show_col_types =</span> <span class="cn">FALSE</span>) <span class="sc">|&gt;</span></span>
<span id="cb4-8"><a href="#cb4-8" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb4-9"><a href="#cb4-9" tabindex="-1"></a>    <span class="co"># StartDate: America/Denver → Asia/Tokyo</span></span>
<span id="cb4-10"><a href="#cb4-10" tabindex="-1"></a>    <span class="at">StartDate =</span> <span class="fu">force_tz</span>(</span>
<span id="cb4-11"><a href="#cb4-11" tabindex="-1"></a>      <span class="fu">as.POSIXct</span>(StartDate, <span class="at">format =</span> <span class="st">&quot;%Y/%m/%d %H:%M&quot;</span>, <span class="at">tz =</span> <span class="st">&quot;America/Denver&quot;</span>),</span>
<span id="cb4-12"><a href="#cb4-12" tabindex="-1"></a>      <span class="at">tzone =</span> <span class="st">&quot;America/Denver&quot;</span></span>
<span id="cb4-13"><a href="#cb4-13" tabindex="-1"></a>    ) <span class="sc">|&gt;</span> <span class="fu">with_tz</span>(<span class="st">&quot;Asia/Tokyo&quot;</span>),</span>
<span id="cb4-14"><a href="#cb4-14" tabindex="-1"></a>    </span>
<span id="cb4-15"><a href="#cb4-15" tabindex="-1"></a>    <span class="at">cutoff_time =</span> .env<span class="sc">$</span>cutoff_time,</span>
<span id="cb4-16"><a href="#cb4-16" tabindex="-1"></a>    </span>
<span id="cb4-17"><a href="#cb4-17" tabindex="-1"></a>    <span class="at">StartTime =</span> StartDate,</span>
<span id="cb4-18"><a href="#cb4-18" tabindex="-1"></a>    </span>
<span id="cb4-19"><a href="#cb4-19" tabindex="-1"></a>    <span class="at">cabap =</span> <span class="fu">case_when</span>(Q1 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb4-20"><a href="#cb4-20" tabindex="-1"></a>                      Q1 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb4-21"><a href="#cb4-21" tabindex="-1"></a>                      Q1 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="cn">NA_real_</span>,</span>
<span id="cb4-22"><a href="#cb4-22" tabindex="-1"></a>                      <span class="cn">TRUE</span>    <span class="sc">~</span> <span class="cn">NA_real_</span>),</span>
<span id="cb4-23"><a href="#cb4-23" tabindex="-1"></a>    </span>
<span id="cb4-24"><a href="#cb4-24" tabindex="-1"></a>    <span class="at">exposure =</span> <span class="fu">case_when</span>(Q5_4 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb4-25"><a href="#cb4-25" tabindex="-1"></a>                         <span class="cn">TRUE</span>       <span class="sc">~</span> <span class="dv">0</span>),</span>
<span id="cb4-26"><a href="#cb4-26" tabindex="-1"></a>    </span>
<span id="cb4-27"><a href="#cb4-27" tabindex="-1"></a>    <span class="at">ftj  =</span> Q3_11,</span>
<span id="cb4-28"><a href="#cb4-28" tabindex="-1"></a>    <span class="at">ftl  =</span> Q3_1,</span>
<span id="cb4-29"><a href="#cb4-29" tabindex="-1"></a>    <span class="at">ftki =</span> Q3_6,</span>
<span id="cb4-30"><a href="#cb4-30" tabindex="-1"></a>    </span>
<span id="cb4-31"><a href="#cb4-31" tabindex="-1"></a>    <span class="at">busi =</span> <span class="fu">case_when</span>(Q7_1...<span class="dv">30</span> <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">5</span>,</span>
<span id="cb4-32"><a href="#cb4-32" tabindex="-1"></a>                     Q7_1...<span class="dv">30</span> <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">4</span>,</span>
<span id="cb4-33"><a href="#cb4-33" tabindex="-1"></a>                     Q7_1...<span class="dv">30</span> <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="dv">2</span>,</span>
<span id="cb4-34"><a href="#cb4-34" tabindex="-1"></a>                     Q7_1...<span class="dv">30</span> <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb4-35"><a href="#cb4-35" tabindex="-1"></a>                     <span class="cn">TRUE</span>      <span class="sc">~</span> <span class="dv">3</span>),</span>
<span id="cb4-36"><a href="#cb4-36" tabindex="-1"></a>    <span class="at">liv  =</span> <span class="fu">case_when</span>(Q7_2...<span class="dv">31</span> <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">5</span>,</span>
<span id="cb4-37"><a href="#cb4-37" tabindex="-1"></a>                     Q7_2...<span class="dv">31</span> <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">4</span>,</span>
<span id="cb4-38"><a href="#cb4-38" tabindex="-1"></a>                     Q7_2...<span class="dv">31</span> <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="dv">2</span>,</span>
<span id="cb4-39"><a href="#cb4-39" tabindex="-1"></a>                     Q7_2...<span class="dv">31</span> <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb4-40"><a href="#cb4-40" tabindex="-1"></a>                     <span class="cn">TRUE</span>      <span class="sc">~</span> <span class="dv">3</span>),</span>
<span id="cb4-41"><a href="#cb4-41" tabindex="-1"></a>    </span>
<span id="cb4-42"><a href="#cb4-42" tabindex="-1"></a>    <span class="at">psu_rul =</span> <span class="fu">case_when</span>(Q2 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">14</span>,<span class="dv">15</span>) <span class="sc">~</span> <span class="cn">NA_real_</span>,</span>
<span id="cb4-43"><a href="#cb4-43" tabindex="-1"></a>                        Q2 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">4</span>)    <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb4-44"><a href="#cb4-44" tabindex="-1"></a>                        <span class="cn">TRUE</span>              <span class="sc">~</span> <span class="dv">0</span>),</span>
<span id="cb4-45"><a href="#cb4-45" tabindex="-1"></a>    </span>
<span id="cb4-46"><a href="#cb4-46" tabindex="-1"></a>    <span class="at">age       =</span> Q10,</span>
<span id="cb4-47"><a href="#cb4-47" tabindex="-1"></a>    <span class="at">female    =</span> <span class="fu">case_when</span>(Q9 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb4-48"><a href="#cb4-48" tabindex="-1"></a>                          Q9 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb4-49"><a href="#cb4-49" tabindex="-1"></a>                          Q9 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="cn">NA_real_</span>,</span>
<span id="cb4-50"><a href="#cb4-50" tabindex="-1"></a>                          <span class="cn">TRUE</span>     <span class="sc">~</span> <span class="cn">NA_real_</span>),</span>
<span id="cb4-51"><a href="#cb4-51" tabindex="-1"></a>    <span class="at">education =</span> <span class="fu">na_if</span>(Q11, <span class="dv">5</span>),</span>
<span id="cb4-52"><a href="#cb4-52" tabindex="-1"></a>    <span class="at">income    =</span> <span class="fu">if_else</span>(Q12 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">15</span>,<span class="dv">16</span>), <span class="cn">NA</span>, Q12),</span>
<span id="cb4-53"><a href="#cb4-53" tabindex="-1"></a>    </span>
<span id="cb4-54"><a href="#cb4-54" tabindex="-1"></a>    <span class="at">cutoff      =</span> <span class="fu">as.integer</span>(StartTime <span class="sc">&gt;=</span> cutoff_time),</span>
<span id="cb4-55"><a href="#cb4-55" tabindex="-1"></a>    <span class="at">running_var =</span> <span class="fu">as.numeric</span>(<span class="fu">difftime</span>(StartDate, cutoff_time, <span class="at">units =</span> <span class="st">&quot;mins&quot;</span>)),</span>
<span id="cb4-56"><a href="#cb4-56" tabindex="-1"></a>    <span class="at">running_var2 =</span> running_var <span class="sc">+</span> <span class="fu">rnorm</span>(<span class="fu">length</span>(running_var), <span class="at">mean =</span> <span class="dv">0</span>, <span class="at">sd =</span> <span class="fl">0.01</span>)</span>
<span id="cb4-57"><a href="#cb4-57" tabindex="-1"></a>  )</span>
<span id="cb4-58"><a href="#cb4-58" tabindex="-1"></a></span>
<span id="cb4-59"><a href="#cb4-59" tabindex="-1"></a>df_rdd66 <span class="ot">&lt;-</span> judo66p <span class="sc">|&gt;</span></span>
<span id="cb4-60"><a href="#cb4-60" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(</span>
<span id="cb4-61"><a href="#cb4-61" tabindex="-1"></a>    StartDate, StartTime,</span>
<span id="cb4-62"><a href="#cb4-62" tabindex="-1"></a>    Q1, cabap, ftj, busi, liv, ftki, ftl, exposure,</span>
<span id="cb4-63"><a href="#cb4-63" tabindex="-1"></a>    cutoff, age, female, education, income, psu_rul,</span>
<span id="cb4-64"><a href="#cb4-64" tabindex="-1"></a>    <span class="at">X1 =</span> Q7_1...<span class="dv">30</span>, <span class="at">X2 =</span> Q7_2...<span class="dv">31</span>, <span class="at">X3 =</span> Q7_3, <span class="at">X4 =</span> Q7_4, <span class="at">X5 =</span> Q7_5, <span class="at">X6 =</span> Q7_6, cutoff_time,</span>
<span id="cb4-65"><a href="#cb4-65" tabindex="-1"></a>    running_var, running_var2</span>
<span id="cb4-66"><a href="#cb4-66" tabindex="-1"></a>  ) <span class="sc">|&gt;</span></span>
<span id="cb4-67"><a href="#cb4-67" tabindex="-1"></a>  tidyr<span class="sc">::</span><span class="fu">drop_na</span>()</span>
<span id="cb4-68"><a href="#cb4-68" tabindex="-1"></a></span>
<span id="cb4-69"><a href="#cb4-69" tabindex="-1"></a>fa_result <span class="ot">&lt;-</span> df_rdd66 <span class="sc">|&gt;</span></span>
<span id="cb4-70"><a href="#cb4-70" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(X1, X2, X3, X4, X5,X6) <span class="sc">|&gt;</span></span>
<span id="cb4-71"><a href="#cb4-71" tabindex="-1"></a>  psych<span class="sc">::</span><span class="fu">fa</span>(<span class="at">nfactors =</span> <span class="dv">2</span>, <span class="at">rotate =</span> <span class="st">&quot;varimax&quot;</span>, <span class="at">scores =</span> <span class="st">&quot;regression&quot;</span>)</span>
<span id="cb4-72"><a href="#cb4-72" tabindex="-1"></a></span>
<span id="cb4-73"><a href="#cb4-73" tabindex="-1"></a>df_rdd66 <span class="ot">&lt;-</span> df_rdd66 <span class="sc">|&gt;</span></span>
<span id="cb4-74"><a href="#cb4-74" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">patriotism =</span> <span class="fu">as.numeric</span>(fa_result<span class="sc">$</span>scores[, <span class="dv">1</span>]))</span>
<span id="cb4-75"><a href="#cb4-75" tabindex="-1"></a></span>
<span id="cb4-76"><a href="#cb4-76" tabindex="-1"></a></span>
<span id="cb4-77"><a href="#cb4-77" tabindex="-1"></a></span>
<span id="cb4-78"><a href="#cb4-78" tabindex="-1"></a>cutoff_range  <span class="ot">&lt;-</span> <span class="fu">range</span>(df_rdd66<span class="sc">$</span>running_var,  <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb4-79"><a href="#cb4-79" tabindex="-1"></a>cutoff_range2 <span class="ot">&lt;-</span> <span class="fu">range</span>(df_rdd66<span class="sc">$</span>running_var2, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb4-80"><a href="#cb4-80" tabindex="-1"></a></span>
<span id="cb4-81"><a href="#cb4-81" tabindex="-1"></a><span class="fu">dim</span>(df_rdd66)</span></code></pre></div>
<pre><code>## [1] 712  26</code></pre>
</div>
</div>
<div id="code-for-the-main-text-analyses" class="section level1 unnumbered">
<h1 class="unnumbered">Code for the Main-Text Analyses</h1>
<div id="empirical-contexts" class="section level2 unnumbered">
<h2 class="unnumbered">Empirical contexts</h2>
<div id="figure-1-volleyball-and-judo-garner-more-interest-from-japanese-respondents" class="section level3 unnumbered">
<h3 class="unnumbered">Figure 1: Volleyball and judo garner more
interest from Japanese respondents</h3>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a>df <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;cmop.csv&quot;</span>)</span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a></span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a>map_q5_var <span class="ot">&lt;-</span> tibble<span class="sc">::</span><span class="fu">tibble</span>(</span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>  <span class="at">q5  =</span> <span class="fu">c</span>(<span class="st">&quot;Q5_1&quot;</span>,<span class="st">&quot;Q5_2&quot;</span>,<span class="st">&quot;Q5_4&quot;</span>,<span class="st">&quot;Q5_5&quot;</span>,<span class="st">&quot;Q5_6&quot;</span>,</span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>          <span class="st">&quot;Q5_7&quot;</span>,<span class="st">&quot;Q5_8&quot;</span>,<span class="st">&quot;Q5_9&quot;</span>,<span class="st">&quot;Q5_10&quot;</span>,<span class="st">&quot;Q5_11&quot;</span>),</span>
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a>  <span class="at">var =</span> <span class="fu">paste0</span>(<span class="st">&quot;var&quot;</span>, <span class="dv">1</span><span class="sc">:</span><span class="dv">10</span>)</span>
<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a>)</span>
<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a></span>
<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a>event_labels_en <span class="ot">&lt;-</span> <span class="fu">c</span>(</span>
<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a>  <span class="st">&quot;女子 やり投げ&quot;</span>        <span class="ot">=</span> <span class="st">&quot;Women&#39;s Javelin Throw&quot;</span>,</span>
<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a>  <span class="st">&quot;男子 やり投げ&quot;</span>        <span class="ot">=</span> <span class="st">&quot;Men&#39;s Javelin Throw&quot;</span>,</span>
<span id="cb6-12"><a href="#cb6-12" tabindex="-1"></a>  <span class="st">&quot;女子 体操&quot;</span>            <span class="ot">=</span> <span class="st">&quot;Women&#39;s Artistic Gymnastics&quot;</span>,</span>
<span id="cb6-13"><a href="#cb6-13" tabindex="-1"></a>  <span class="st">&quot;男子 体操&quot;</span>            <span class="ot">=</span> <span class="st">&quot;Men&#39;s Artistic Gymnastics&quot;</span>,</span>
<span id="cb6-14"><a href="#cb6-14" tabindex="-1"></a>  <span class="st">&quot;女子 レスリング&quot;</span>      <span class="ot">=</span> <span class="st">&quot;Women&#39;s Wrestling&quot;</span>,</span>
<span id="cb6-15"><a href="#cb6-15" tabindex="-1"></a>  <span class="st">&quot;男子 レスリング&quot;</span>      <span class="ot">=</span> <span class="st">&quot;Men&#39;s Wrestling&quot;</span>,</span>
<span id="cb6-16"><a href="#cb6-16" tabindex="-1"></a>  <span class="st">&quot;女子 柔道&quot;</span>            <span class="ot">=</span> <span class="st">&quot;Women&#39;s Judo&quot;</span>,</span>
<span id="cb6-17"><a href="#cb6-17" tabindex="-1"></a>  <span class="st">&quot;男子 柔道&quot;</span>            <span class="ot">=</span> <span class="st">&quot;Men&#39;s Judo&quot;</span>,</span>
<span id="cb6-18"><a href="#cb6-18" tabindex="-1"></a>  <span class="st">&quot;女子 ブレイキン&quot;</span>      <span class="ot">=</span> <span class="st">&quot;Women&#39;s Breaking&quot;</span>,</span>
<span id="cb6-19"><a href="#cb6-19" tabindex="-1"></a>  <span class="st">&quot;男子 ブレイキン&quot;</span>      <span class="ot">=</span> <span class="st">&quot;Men&#39;s Breaking&quot;</span>,</span>
<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a>  <span class="st">&quot;女子 セーリング&quot;</span>      <span class="ot">=</span> <span class="st">&quot;Women&#39;s Sailing&quot;</span>,</span>
<span id="cb6-21"><a href="#cb6-21" tabindex="-1"></a>  <span class="st">&quot;男子 セーリング&quot;</span>      <span class="ot">=</span> <span class="st">&quot;Men&#39;s Sailing&quot;</span>,</span>
<span id="cb6-22"><a href="#cb6-22" tabindex="-1"></a>  <span class="st">&quot;女子 卓球&quot;</span>            <span class="ot">=</span> <span class="st">&quot;Women&#39;s Table Tennis&quot;</span>,</span>
<span id="cb6-23"><a href="#cb6-23" tabindex="-1"></a>  <span class="st">&quot;男子 卓球&quot;</span>            <span class="ot">=</span> <span class="st">&quot;Men&#39;s Table Tennis&quot;</span>,</span>
<span id="cb6-24"><a href="#cb6-24" tabindex="-1"></a>  <span class="st">&quot;女子 フェンシング&quot;</span>    <span class="ot">=</span> <span class="st">&quot;Women&#39;s Fencing&quot;</span>,</span>
<span id="cb6-25"><a href="#cb6-25" tabindex="-1"></a>  <span class="st">&quot;男子 フェンシング&quot;</span>    <span class="ot">=</span> <span class="st">&quot;Men&#39;s Fencing&quot;</span>,</span>
<span id="cb6-26"><a href="#cb6-26" tabindex="-1"></a>  <span class="st">&quot;女子 スケートボード&quot;</span>  <span class="ot">=</span> <span class="st">&quot;Women&#39;s Skateboarding&quot;</span>,</span>
<span id="cb6-27"><a href="#cb6-27" tabindex="-1"></a>  <span class="st">&quot;男子 スケートボード&quot;</span>  <span class="ot">=</span> <span class="st">&quot;Men&#39;s Skateboarding&quot;</span>,</span>
<span id="cb6-28"><a href="#cb6-28" tabindex="-1"></a>  <span class="st">&quot;女子 バレーボール&quot;</span>    <span class="ot">=</span> <span class="st">&quot;Women&#39;s Volleyball&quot;</span>,</span>
<span id="cb6-29"><a href="#cb6-29" tabindex="-1"></a>  <span class="st">&quot;男子 バレーボール&quot;</span>    <span class="ot">=</span> <span class="st">&quot;Men&#39;s Volleyball&quot;</span></span>
<span id="cb6-30"><a href="#cb6-30" tabindex="-1"></a>)</span>
<span id="cb6-31"><a href="#cb6-31" tabindex="-1"></a></span>
<span id="cb6-32"><a href="#cb6-32" tabindex="-1"></a>long_scores <span class="ot">&lt;-</span> purrr<span class="sc">::</span><span class="fu">map2_dfr</span>(</span>
<span id="cb6-33"><a href="#cb6-33" tabindex="-1"></a>  map_q5_var<span class="sc">$</span>q5, map_q5_var<span class="sc">$</span>var,</span>
<span id="cb6-34"><a href="#cb6-34" tabindex="-1"></a>  <span class="sc">~</span> df <span class="sc">%&gt;%</span></span>
<span id="cb6-35"><a href="#cb6-35" tabindex="-1"></a>    <span class="fu">transmute</span>(</span>
<span id="cb6-36"><a href="#cb6-36" tabindex="-1"></a>      <span class="at">event =</span> .data[[.y]],           </span>
<span id="cb6-37"><a href="#cb6-37" tabindex="-1"></a>      <span class="at">score =</span> <span class="fu">as.numeric</span>(.data[[.x]]) </span>
<span id="cb6-38"><a href="#cb6-38" tabindex="-1"></a>    )</span>
<span id="cb6-39"><a href="#cb6-39" tabindex="-1"></a>) <span class="sc">%&gt;%</span></span>
<span id="cb6-40"><a href="#cb6-40" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(score), event <span class="sc">!=</span> <span class="st">&quot;&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb6-41"><a href="#cb6-41" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb6-42"><a href="#cb6-42" tabindex="-1"></a>    <span class="at">event =</span> dplyr<span class="sc">::</span><span class="fu">recode</span>(event, <span class="sc">!!!</span>event_labels_en) </span>
<span id="cb6-43"><a href="#cb6-43" tabindex="-1"></a>  )</span>
<span id="cb6-44"><a href="#cb6-44" tabindex="-1"></a></span>
<span id="cb6-45"><a href="#cb6-45" tabindex="-1"></a>event_mean <span class="ot">&lt;-</span> long_scores <span class="sc">%&gt;%</span></span>
<span id="cb6-46"><a href="#cb6-46" tabindex="-1"></a>  <span class="fu">group_by</span>(event) <span class="sc">%&gt;%</span></span>
<span id="cb6-47"><a href="#cb6-47" tabindex="-1"></a>  <span class="fu">summarise</span>(</span>
<span id="cb6-48"><a href="#cb6-48" tabindex="-1"></a>    <span class="at">mean_score =</span> <span class="fu">mean</span>(score, <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb6-49"><a href="#cb6-49" tabindex="-1"></a>    <span class="at">n =</span> <span class="fu">n</span>(),</span>
<span id="cb6-50"><a href="#cb6-50" tabindex="-1"></a>    <span class="at">.groups =</span> <span class="st">&quot;drop&quot;</span></span>
<span id="cb6-51"><a href="#cb6-51" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
<span id="cb6-52"><a href="#cb6-52" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(mean_score))</span>
<span id="cb6-53"><a href="#cb6-53" tabindex="-1"></a></span>
<span id="cb6-54"><a href="#cb6-54" tabindex="-1"></a><span class="fu">ggplot</span>(event_mean,</span>
<span id="cb6-55"><a href="#cb6-55" tabindex="-1"></a>       <span class="fu">aes</span>(<span class="at">x =</span> <span class="fu">reorder</span>(event, mean_score),</span>
<span id="cb6-56"><a href="#cb6-56" tabindex="-1"></a>           <span class="at">y =</span> mean_score,</span>
<span id="cb6-57"><a href="#cb6-57" tabindex="-1"></a>           <span class="at">fill =</span> mean_score)) <span class="sc">+</span></span>
<span id="cb6-58"><a href="#cb6-58" tabindex="-1"></a>  <span class="fu">geom_col</span>() <span class="sc">+</span></span>
<span id="cb6-59"><a href="#cb6-59" tabindex="-1"></a>  <span class="fu">coord_flip</span>() <span class="sc">+</span></span>
<span id="cb6-60"><a href="#cb6-60" tabindex="-1"></a>  <span class="fu">scale_fill_gradient</span>(</span>
<span id="cb6-61"><a href="#cb6-61" tabindex="-1"></a>    <span class="at">low  =</span> <span class="st">&quot;lightblue&quot;</span>,</span>
<span id="cb6-62"><a href="#cb6-62" tabindex="-1"></a>    <span class="at">high =</span> <span class="st">&quot;navy&quot;</span></span>
<span id="cb6-63"><a href="#cb6-63" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb6-64"><a href="#cb6-64" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb6-65"><a href="#cb6-65" tabindex="-1"></a>    <span class="at">x   =</span> <span class="cn">NULL</span>,</span>
<span id="cb6-66"><a href="#cb6-66" tabindex="-1"></a>    <span class="at">y   =</span> <span class="st">&quot;Average Score&quot;</span>,</span>
<span id="cb6-67"><a href="#cb6-67" tabindex="-1"></a>    <span class="at">fill =</span> <span class="st">&quot;Average Score&quot;</span></span>
<span id="cb6-68"><a href="#cb6-68" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb6-69"><a href="#cb6-69" tabindex="-1"></a>  <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb6-70"><a href="#cb6-70" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb6-71"><a href="#cb6-71" tabindex="-1"></a>    <span class="at">axis.text.y =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">8</span>),</span>
<span id="cb6-72"><a href="#cb6-72" tabindex="-1"></a>    <span class="at">legend.position   =</span> <span class="fu">c</span>(<span class="fl">0.8</span>, <span class="fl">0.2</span>),           </span>
<span id="cb6-73"><a href="#cb6-73" tabindex="-1"></a>    <span class="at">legend.background =</span> <span class="fu">element_rect</span>(</span>
<span id="cb6-74"><a href="#cb6-74" tabindex="-1"></a>      <span class="at">fill   =</span> <span class="st">&quot;white&quot;</span>,</span>
<span id="cb6-75"><a href="#cb6-75" tabindex="-1"></a>      <span class="at">colour =</span> <span class="st">&quot;black&quot;</span></span>
<span id="cb6-76"><a href="#cb6-76" tabindex="-1"></a>    )</span>
<span id="cb6-77"><a href="#cb6-77" tabindex="-1"></a>  )</span></code></pre></div>
<p><img role="img" 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" width="672" /></p>
</div>
<div id="figure-2-declining-cabinet-approval-increasing-cabinet-disapproval-of-the-kishida-administration" class="section level3 unnumbered">
<h3 class="unnumbered">Figure 2: Declining Cabinet Approval, Increasing
Cabinet Disapproval of the Kishida administration</h3>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a>cabap <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;cabap.csv&quot;</span>, <span class="at">stringsAsFactors =</span> <span class="cn">FALSE</span>)</span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a></span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a>cabap <span class="ot">&lt;-</span> cabap <span class="sc">%&gt;%</span></span>
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">Date =</span> <span class="fu">as.Date</span>(<span class="fu">trimws</span>(Date), <span class="at">format =</span> <span class="st">&quot;%Y-%m-%d&quot;</span>))</span>
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a></span>
<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a>cabap_long <span class="ot">&lt;-</span> cabap <span class="sc">%&gt;%</span></span>
<span id="cb7-7"><a href="#cb7-7" tabindex="-1"></a>    <span class="fu">pivot_longer</span>(</span>
<span id="cb7-8"><a href="#cb7-8" tabindex="-1"></a>        <span class="at">cols =</span> <span class="fu">c</span>(Approve, Disapprove),</span>
<span id="cb7-9"><a href="#cb7-9" tabindex="-1"></a>        <span class="at">names_to  =</span> <span class="st">&quot;Cabinet&quot;</span>,</span>
<span id="cb7-10"><a href="#cb7-10" tabindex="-1"></a>        <span class="at">values_to =</span> <span class="st">&quot;Percentage&quot;</span></span>
<span id="cb7-11"><a href="#cb7-11" tabindex="-1"></a>    )</span>
<span id="cb7-12"><a href="#cb7-12" tabindex="-1"></a></span>
<span id="cb7-13"><a href="#cb7-13" tabindex="-1"></a>paris_day    <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(<span class="st">&quot;2024-07-01&quot;</span>)</span>
<span id="cb7-14"><a href="#cb7-14" tabindex="-1"></a>cabinet_day  <span class="ot">&lt;-</span> <span class="fu">as.Date</span>(<span class="st">&quot;2024-10-01&quot;</span>)</span>
<span id="cb7-15"><a href="#cb7-15" tabindex="-1"></a></span>
<span id="cb7-16"><a href="#cb7-16" tabindex="-1"></a><span class="fu">ggplot</span>(cabap_long,</span>
<span id="cb7-17"><a href="#cb7-17" tabindex="-1"></a>            <span class="fu">aes</span>(<span class="at">x =</span> Date, <span class="at">y =</span> Percentage,</span>
<span id="cb7-18"><a href="#cb7-18" tabindex="-1"></a>                <span class="at">colour =</span> Cabinet, <span class="at">group =</span> Cabinet)) <span class="sc">+</span></span>
<span id="cb7-19"><a href="#cb7-19" tabindex="-1"></a>    <span class="fu">geom_line</span>(<span class="at">size =</span> <span class="fl">0.7</span>) <span class="sc">+</span></span>
<span id="cb7-20"><a href="#cb7-20" tabindex="-1"></a>    <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb7-21"><a href="#cb7-21" tabindex="-1"></a>    <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label =</span> Percentage),</span>
<span id="cb7-22"><a href="#cb7-22" tabindex="-1"></a>              <span class="at">vjust =</span> <span class="sc">-</span><span class="fl">0.7</span>, <span class="at">size =</span> <span class="dv">3</span>, <span class="at">show.legend =</span> <span class="cn">FALSE</span>) <span class="sc">+</span></span>
<span id="cb7-23"><a href="#cb7-23" tabindex="-1"></a>    </span>
<span id="cb7-24"><a href="#cb7-24" tabindex="-1"></a>    <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> paris_day,</span>
<span id="cb7-25"><a href="#cb7-25" tabindex="-1"></a>               <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="at">colour =</span> <span class="st">&quot;black&quot;</span>) <span class="sc">+</span></span>
<span id="cb7-26"><a href="#cb7-26" tabindex="-1"></a>    <span class="fu">annotate</span>(<span class="st">&quot;text&quot;</span>,</span>
<span id="cb7-27"><a href="#cb7-27" tabindex="-1"></a>             <span class="at">x =</span> paris_day,</span>
<span id="cb7-28"><a href="#cb7-28" tabindex="-1"></a>             <span class="at">y =</span> <span class="dv">40</span>,              </span>
<span id="cb7-29"><a href="#cb7-29" tabindex="-1"></a>             <span class="at">label =</span> <span class="st">&quot;Paris Olympics&quot;</span>,</span>
<span id="cb7-30"><a href="#cb7-30" tabindex="-1"></a>             <span class="at">angle =</span> <span class="dv">90</span>,</span>
<span id="cb7-31"><a href="#cb7-31" tabindex="-1"></a>             <span class="at">vjust =</span> <span class="sc">-</span><span class="fl">0.3</span>,</span>
<span id="cb7-32"><a href="#cb7-32" tabindex="-1"></a>             <span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb7-33"><a href="#cb7-33" tabindex="-1"></a>    </span>
<span id="cb7-34"><a href="#cb7-34" tabindex="-1"></a>    <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> cabinet_day,</span>
<span id="cb7-35"><a href="#cb7-35" tabindex="-1"></a>               <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="at">colour =</span> <span class="st">&quot;black&quot;</span>) <span class="sc">+</span></span>
<span id="cb7-36"><a href="#cb7-36" tabindex="-1"></a>    <span class="fu">annotate</span>(<span class="st">&quot;text&quot;</span>,</span>
<span id="cb7-37"><a href="#cb7-37" tabindex="-1"></a>             <span class="at">x =</span> cabinet_day,</span>
<span id="cb7-38"><a href="#cb7-38" tabindex="-1"></a>             <span class="at">y =</span> <span class="dv">40</span>,</span>
<span id="cb7-39"><a href="#cb7-39" tabindex="-1"></a>             <span class="at">label =</span> <span class="st">&quot;Cabinet change (Kishida to Ishiba)&quot;</span>,</span>
<span id="cb7-40"><a href="#cb7-40" tabindex="-1"></a>             <span class="at">angle =</span> <span class="dv">90</span>,</span>
<span id="cb7-41"><a href="#cb7-41" tabindex="-1"></a>             <span class="at">vjust =</span> <span class="sc">-</span><span class="fl">0.3</span>,</span>
<span id="cb7-42"><a href="#cb7-42" tabindex="-1"></a>             <span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb7-43"><a href="#cb7-43" tabindex="-1"></a>    </span>
<span id="cb7-44"><a href="#cb7-44" tabindex="-1"></a>    <span class="fu">scale_colour_manual</span>(</span>
<span id="cb7-45"><a href="#cb7-45" tabindex="-1"></a>        <span class="at">name   =</span> <span class="st">&quot;Cabinet&quot;</span>,</span>
<span id="cb7-46"><a href="#cb7-46" tabindex="-1"></a>        <span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;Approve&quot;</span> <span class="ot">=</span> <span class="st">&quot;blue&quot;</span>,</span>
<span id="cb7-47"><a href="#cb7-47" tabindex="-1"></a>                   <span class="st">&quot;Disapprove&quot;</span> <span class="ot">=</span> <span class="st">&quot;red&quot;</span>)</span>
<span id="cb7-48"><a href="#cb7-48" tabindex="-1"></a>    ) <span class="sc">+</span></span>
<span id="cb7-49"><a href="#cb7-49" tabindex="-1"></a>    <span class="fu">scale_x_date</span>(</span>
<span id="cb7-50"><a href="#cb7-50" tabindex="-1"></a>        <span class="at">date_labels =</span> <span class="st">&quot;%Y-%m&quot;</span>,</span>
<span id="cb7-51"><a href="#cb7-51" tabindex="-1"></a>        <span class="at">date_breaks =</span> <span class="st">&quot;2 month&quot;</span>,</span>
<span id="cb7-52"><a href="#cb7-52" tabindex="-1"></a>        <span class="at">limits =</span> <span class="fu">c</span>(<span class="fu">min</span>(cabap<span class="sc">$</span>Date),</span>
<span id="cb7-53"><a href="#cb7-53" tabindex="-1"></a>                   <span class="fu">as.Date</span>(<span class="st">&quot;2024-10-31&quot;</span>))   </span>
<span id="cb7-54"><a href="#cb7-54" tabindex="-1"></a>    ) <span class="sc">+</span></span>
<span id="cb7-55"><a href="#cb7-55" tabindex="-1"></a>    <span class="fu">coord_cartesian</span>(<span class="at">ylim =</span> <span class="fu">c</span>(<span class="dv">18</span>, <span class="dv">62</span>)) <span class="sc">+</span></span>
<span id="cb7-56"><a href="#cb7-56" tabindex="-1"></a>    <span class="fu">labs</span>(</span>
<span id="cb7-57"><a href="#cb7-57" tabindex="-1"></a>        <span class="at">title =</span> <span class="st">&quot;Cabinet Approval and Disapproval Ratings&quot;</span>,</span>
<span id="cb7-58"><a href="#cb7-58" tabindex="-1"></a>        <span class="at">x     =</span> <span class="st">&quot;Date&quot;</span>,</span>
<span id="cb7-59"><a href="#cb7-59" tabindex="-1"></a>        <span class="at">y     =</span> <span class="st">&quot;Percentage&quot;</span></span>
<span id="cb7-60"><a href="#cb7-60" tabindex="-1"></a>    ) <span class="sc">+</span></span>
<span id="cb7-61"><a href="#cb7-61" tabindex="-1"></a>    <span class="fu">theme_bw</span>() <span class="sc">+</span></span>
<span id="cb7-62"><a href="#cb7-62" tabindex="-1"></a>    <span class="fu">theme</span>(</span>
<span id="cb7-63"><a href="#cb7-63" tabindex="-1"></a>        <span class="at">axis.text.x        =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">hjust =</span> <span class="dv">1</span>),</span>
<span id="cb7-64"><a href="#cb7-64" tabindex="-1"></a>        <span class="at">legend.position    =</span> <span class="fu">c</span>(<span class="fl">0.15</span>, <span class="fl">0.4</span>),   </span>
<span id="cb7-65"><a href="#cb7-65" tabindex="-1"></a>        <span class="at">legend.background  =</span> <span class="fu">element_rect</span>(<span class="at">fill =</span> <span class="st">&quot;white&quot;</span>, <span class="at">colour =</span> <span class="st">&quot;black&quot;</span>),</span>
<span id="cb7-66"><a href="#cb7-66" tabindex="-1"></a>        <span class="at">legend.title       =</span> <span class="fu">element_text</span>(<span class="at">face =</span> <span class="st">&quot;bold&quot;</span>)</span>
<span id="cb7-67"><a href="#cb7-67" tabindex="-1"></a>    )</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
</div>
</div>
<div id="basic-regression-discontinuity-design-analysis" class="section level2 unnumbered">
<h2 class="unnumbered">Basic regression discontinuity design
analysis</h2>
<div id="table-1-even-the-first-gold-medal-womens-judo-48kg-does-not-increase-cabinet-approval-and-thermometer-to-government" class="section level3 unnumbered">
<h3 class="unnumbered">Table 1: Even the first gold medal (Women’s Judo
48kg) does not increase cabinet approval and thermometer to
government</h3>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> <span class="fu">cbind</span>(df_rdd48<span class="sc">$</span>age, df_rdd48<span class="sc">$</span>female, df_rdd48<span class="sc">$</span>income, df_rdd48<span class="sc">$</span>education, df_rdd48<span class="sc">$</span>psu_rul)</span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a></span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a>results_judo48p <span class="ot">&lt;-</span> <span class="fu">list</span>()</span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a></span>
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a>outcome_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;cabap&quot;</span>, <span class="st">&quot;ftj&quot;</span>)</span>
<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a>outcome_prefix <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;c&quot;</span>, <span class="st">&quot;f&quot;</span>)</span>
<span id="cb8-7"><a href="#cb8-7" tabindex="-1"></a></span>
<span id="cb8-8"><a href="#cb8-8" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(outcome_vars)) {</span>
<span id="cb8-9"><a href="#cb8-9" tabindex="-1"></a>  outcome_var <span class="ot">&lt;-</span> outcome_vars[i]</span>
<span id="cb8-10"><a href="#cb8-10" tabindex="-1"></a>  prefix <span class="ot">&lt;-</span> outcome_prefix[i]</span>
<span id="cb8-11"><a href="#cb8-11" tabindex="-1"></a>  </span>
<span id="cb8-12"><a href="#cb8-12" tabindex="-1"></a>  safe_rdrobust <span class="ot">&lt;-</span> <span class="cf">function</span>(..., prefix_suffix) {</span>
<span id="cb8-13"><a href="#cb8-13" tabindex="-1"></a>    <span class="fu">tryCatch</span>({</span>
<span id="cb8-14"><a href="#cb8-14" tabindex="-1"></a>      <span class="fu">rdrobust</span>(..., <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb8-15"><a href="#cb8-15" tabindex="-1"></a>    }, <span class="at">error =</span> <span class="cf">function</span>(e) {</span>
<span id="cb8-16"><a href="#cb8-16" tabindex="-1"></a>      <span class="fu">message</span>(<span class="fu">paste0</span>(<span class="st">&quot;Error in &quot;</span>, prefix_suffix, <span class="st">&quot;: &quot;</span>, e<span class="sc">$</span>message))</span>
<span id="cb8-17"><a href="#cb8-17" tabindex="-1"></a>      <span class="cn">NULL</span></span>
<span id="cb8-18"><a href="#cb8-18" tabindex="-1"></a>    })</span>
<span id="cb8-19"><a href="#cb8-19" tabindex="-1"></a>  }</span>
<span id="cb8-20"><a href="#cb8-20" tabindex="-1"></a>  </span>
<span id="cb8-21"><a href="#cb8-21" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res1&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd48[[outcome_var]], df_rdd48<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res1&quot;</span>, prefix))</span>
<span id="cb8-22"><a href="#cb8-22" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res2&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd48[[outcome_var]], df_rdd48<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;msesum&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res2&quot;</span>, prefix))</span>
<span id="cb8-23"><a href="#cb8-23" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res3&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd48[[outcome_var]], df_rdd48<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res3&quot;</span>, prefix))</span>
<span id="cb8-24"><a href="#cb8-24" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res4&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd48[[outcome_var]], df_rdd48<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;uni&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res4&quot;</span>, prefix))</span>
<span id="cb8-25"><a href="#cb8-25" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res5&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd48[[outcome_var]], df_rdd48<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;cerrd&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res5&quot;</span>, prefix))</span>
<span id="cb8-26"><a href="#cb8-26" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res6&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd48[[outcome_var]], df_rdd48<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">q =</span> <span class="dv">2</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res6&quot;</span>, prefix))</span>
<span id="cb8-27"><a href="#cb8-27" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res7&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd48[[outcome_var]], df_rdd48<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">p =</span> <span class="dv">2</span>, <span class="at">q =</span> <span class="dv">3</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res7&quot;</span>, prefix))</span>
<span id="cb8-28"><a href="#cb8-28" tabindex="-1"></a>}</span>
<span id="cb8-29"><a href="#cb8-29" tabindex="-1"></a></span>
<span id="cb8-30"><a href="#cb8-30" tabindex="-1"></a></span>
<span id="cb8-31"><a href="#cb8-31" tabindex="-1"></a></span>
<span id="cb8-32"><a href="#cb8-32" tabindex="-1"></a><span class="fu">source</span>(<span class="st">&quot;process_results_rdd.R&quot;</span>)</span>
<span id="cb8-33"><a href="#cb8-33" tabindex="-1"></a></span>
<span id="cb8-34"><a href="#cb8-34" tabindex="-1"></a><span class="co">#cabinet approval</span></span>
<span id="cb8-35"><a href="#cb8-35" tabindex="-1"></a></span>
<span id="cb8-36"><a href="#cb8-36" tabindex="-1"></a>final_results_list <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1c&quot;</span>, <span class="st">&quot;res2c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) {</span>
<span id="cb8-37"><a href="#cb8-37" tabindex="-1"></a>  <span class="fu">process_results</span>(results_judo48p[[res]])</span>
<span id="cb8-38"><a href="#cb8-38" tabindex="-1"></a>})</span>
<span id="cb8-39"><a href="#cb8-39" tabindex="-1"></a></span>
<span id="cb8-40"><a href="#cb8-40" tabindex="-1"></a>final_combined_results <span class="ot">&lt;-</span> <span class="fu">do.call</span>(cbind, <span class="fu">lapply</span>(final_results_list, <span class="cf">function</span>(x) x[, <span class="dv">2</span>]))</span>
<span id="cb8-41"><a href="#cb8-41" tabindex="-1"></a></span>
<span id="cb8-42"><a href="#cb8-42" tabindex="-1"></a>kernel_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1c&quot;</span>, <span class="st">&quot;res3c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) results_judo48p[[res]]<span class="sc">$</span>kernel)</span>
<span id="cb8-43"><a href="#cb8-43" tabindex="-1"></a>bwselect_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res2c&quot;</span>,<span class="st">&quot;res3c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) results_judo48p[[res]]<span class="sc">$</span>bwselect)</span>
<span id="cb8-44"><a href="#cb8-44" tabindex="-1"></a>bwp_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res2c&quot;</span>,<span class="st">&quot;res3c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) <span class="fu">round</span>(results_judo48p[[res]]<span class="sc">$</span>bws[<span class="dv">1</span>,<span class="dv">1</span>], <span class="dv">3</span>))</span>
<span id="cb8-45"><a href="#cb8-45" tabindex="-1"></a>bwb_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res2c&quot;</span>,<span class="st">&quot;res3c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) <span class="fu">round</span>(results_judo48p[[res]]<span class="sc">$</span>bws[<span class="dv">2</span>,<span class="dv">1</span>], <span class="dv">3</span>))</span>
<span id="cb8-46"><a href="#cb8-46" tabindex="-1"></a>pol_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res2c&quot;</span>,<span class="st">&quot;res3c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) results_judo48p[[res]]<span class="sc">$</span>p)</span>
<span id="cb8-47"><a href="#cb8-47" tabindex="-1"></a>q_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res2c&quot;</span>,<span class="st">&quot;res3c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) results_judo48p[[res]]<span class="sc">$</span>q)</span>
<span id="cb8-48"><a href="#cb8-48" tabindex="-1"></a>n_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res2c&quot;</span>,<span class="st">&quot;res3c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) <span class="fu">sum</span>(results_judo48p[[res]]<span class="sc">$</span>N))</span>
<span id="cb8-49"><a href="#cb8-49" tabindex="-1"></a></span>
<span id="cb8-50"><a href="#cb8-50" tabindex="-1"></a>final_combined_results_with_kernel <span class="ot">&lt;-</span> <span class="fu">rbind</span>(</span>
<span id="cb8-51"><a href="#cb8-51" tabindex="-1"></a>  final_combined_results,</span>
<span id="cb8-52"><a href="#cb8-52" tabindex="-1"></a>  n_results,</span>
<span id="cb8-53"><a href="#cb8-53" tabindex="-1"></a>  kernel_results,</span>
<span id="cb8-54"><a href="#cb8-54" tabindex="-1"></a>  bwselect_results,</span>
<span id="cb8-55"><a href="#cb8-55" tabindex="-1"></a>  pol_results,</span>
<span id="cb8-56"><a href="#cb8-56" tabindex="-1"></a>  q_results,</span>
<span id="cb8-57"><a href="#cb8-57" tabindex="-1"></a>  bwp_results,</span>
<span id="cb8-58"><a href="#cb8-58" tabindex="-1"></a>  bwb_results</span>
<span id="cb8-59"><a href="#cb8-59" tabindex="-1"></a>)</span>
<span id="cb8-60"><a href="#cb8-60" tabindex="-1"></a></span>
<span id="cb8-61"><a href="#cb8-61" tabindex="-1"></a>df_rdd48_judo48p <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="fu">c</span>(<span class="st">&quot;Treatment&quot;</span>, <span class="st">&quot;Robust 95% CI&quot;</span>,<span class="st">&quot;Robust $p$-value&quot;</span>, <span class="st">&quot;Observations&quot;</span> , <span class="st">&quot;Kernel type&quot;</span>,</span>
<span id="cb8-62"><a href="#cb8-62" tabindex="-1"></a>                                 <span class="st">&quot;BW type&quot;</span>, <span class="st">&quot;Order loc. poly.($p$)&quot;</span>, <span class="st">&quot;Order bias ($q$)&quot;</span>, <span class="st">&quot;BW loc. poly.($h$)&quot;</span>,</span>
<span id="cb8-63"><a href="#cb8-63" tabindex="-1"></a>                                 <span class="st">&quot;BW bias ($b$)&quot;</span>), final_combined_results_with_kernel)</span>
<span id="cb8-64"><a href="#cb8-64" tabindex="-1"></a></span>
<span id="cb8-65"><a href="#cb8-65" tabindex="-1"></a><span class="fu">colnames</span>(df_rdd48_judo48p) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;&quot;</span>, <span class="st">&quot;(1)&quot;</span>, <span class="st">&quot;(2)&quot;</span>, <span class="st">&quot;(3)&quot;</span>, <span class="st">&quot;(4)&quot;</span>)</span>
<span id="cb8-66"><a href="#cb8-66" tabindex="-1"></a></span>
<span id="cb8-67"><a href="#cb8-67" tabindex="-1"></a></span>
<span id="cb8-68"><a href="#cb8-68" tabindex="-1"></a></span>
<span id="cb8-69"><a href="#cb8-69" tabindex="-1"></a><span class="co">#feeling thermometer</span></span>
<span id="cb8-70"><a href="#cb8-70" tabindex="-1"></a>final_results_list <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) {</span>
<span id="cb8-71"><a href="#cb8-71" tabindex="-1"></a>  <span class="fu">process_results</span>(results_judo48p[[res]])</span>
<span id="cb8-72"><a href="#cb8-72" tabindex="-1"></a>})</span>
<span id="cb8-73"><a href="#cb8-73" tabindex="-1"></a></span>
<span id="cb8-74"><a href="#cb8-74" tabindex="-1"></a>final_combined_results <span class="ot">&lt;-</span> <span class="fu">do.call</span>(cbind, <span class="fu">lapply</span>(final_results_list, <span class="cf">function</span>(x) x[, <span class="dv">2</span>]))</span>
<span id="cb8-75"><a href="#cb8-75" tabindex="-1"></a></span>
<span id="cb8-76"><a href="#cb8-76" tabindex="-1"></a>kernel_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) results_judo48p[[res]]<span class="sc">$</span>kernel)</span>
<span id="cb8-77"><a href="#cb8-77" tabindex="-1"></a>bwselect_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) results_judo48p[[res]]<span class="sc">$</span>bwselect)</span>
<span id="cb8-78"><a href="#cb8-78" tabindex="-1"></a>bwp_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) <span class="fu">round</span>(results_judo48p[[res]]<span class="sc">$</span>bws[<span class="dv">1</span>,<span class="dv">1</span>], <span class="dv">3</span>))</span>
<span id="cb8-79"><a href="#cb8-79" tabindex="-1"></a>bwb_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) <span class="fu">round</span>(results_judo48p[[res]]<span class="sc">$</span>bws[<span class="dv">2</span>,<span class="dv">1</span>], <span class="dv">3</span>))</span>
<span id="cb8-80"><a href="#cb8-80" tabindex="-1"></a>pol_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) results_judo48p[[res]]<span class="sc">$</span>p)</span>
<span id="cb8-81"><a href="#cb8-81" tabindex="-1"></a>q_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) results_judo48p[[res]]<span class="sc">$</span>q)</span>
<span id="cb8-82"><a href="#cb8-82" tabindex="-1"></a>n_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) <span class="fu">sum</span>(results_judo48p[[res]]<span class="sc">$</span>N))</span>
<span id="cb8-83"><a href="#cb8-83" tabindex="-1"></a></span>
<span id="cb8-84"><a href="#cb8-84" tabindex="-1"></a>final_combined_results_with_kernel <span class="ot">&lt;-</span> <span class="fu">rbind</span>(</span>
<span id="cb8-85"><a href="#cb8-85" tabindex="-1"></a>  final_combined_results,</span>
<span id="cb8-86"><a href="#cb8-86" tabindex="-1"></a>  n_results,</span>
<span id="cb8-87"><a href="#cb8-87" tabindex="-1"></a>  kernel_results,</span>
<span id="cb8-88"><a href="#cb8-88" tabindex="-1"></a>  bwselect_results,</span>
<span id="cb8-89"><a href="#cb8-89" tabindex="-1"></a>  pol_results,</span>
<span id="cb8-90"><a href="#cb8-90" tabindex="-1"></a>  q_results,</span>
<span id="cb8-91"><a href="#cb8-91" tabindex="-1"></a>  bwp_results,</span>
<span id="cb8-92"><a href="#cb8-92" tabindex="-1"></a>  bwb_results</span>
<span id="cb8-93"><a href="#cb8-93" tabindex="-1"></a>)</span>
<span id="cb8-94"><a href="#cb8-94" tabindex="-1"></a></span>
<span id="cb8-95"><a href="#cb8-95" tabindex="-1"></a>df_rdd48_judo48pft <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="fu">c</span>(<span class="st">&quot;Treatment&quot;</span>, <span class="st">&quot;Robust 95% CI&quot;</span>,<span class="st">&quot;Robust $p$-value&quot;</span>, <span class="st">&quot;Observations&quot;</span> , <span class="st">&quot;Kernel type&quot;</span>,</span>
<span id="cb8-96"><a href="#cb8-96" tabindex="-1"></a>                                   <span class="st">&quot;BW type&quot;</span>, <span class="st">&quot;Order loc. poly.($p$)&quot;</span>, <span class="st">&quot;Order bias ($q$)&quot;</span>, <span class="st">&quot;BW loc. poly.($h$)&quot;</span>,</span>
<span id="cb8-97"><a href="#cb8-97" tabindex="-1"></a>                                   <span class="st">&quot;BW bias ($b$)&quot;</span>), final_combined_results_with_kernel)</span>
<span id="cb8-98"><a href="#cb8-98" tabindex="-1"></a></span>
<span id="cb8-99"><a href="#cb8-99" tabindex="-1"></a><span class="fu">colnames</span>(df_rdd48_judo48pft) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;&quot;</span>, <span class="st">&quot;(1)&quot;</span>, <span class="st">&quot;(2)&quot;</span>, <span class="st">&quot;(3)&quot;</span>, <span class="st">&quot;(4)&quot;</span>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Treatment
</td>
<td style="text-align:center;">
0.069
</td>
<td style="text-align:center;">
0.070
</td>
<td style="text-align:center;">
0.089
</td>
<td style="text-align:center;">
0.092
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-0.170, 0.308]
</td>
<td style="text-align:center;">
[-0.170, 0.309]
</td>
<td style="text-align:center;">
[-0.137, 0.315]
</td>
<td style="text-align:center;">
[-0.165, 0.349]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.571
</td>
<td style="text-align:center;">
0.570
</td>
<td style="text-align:center;">
0.441
</td>
<td style="text-align:center;">
0.482
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
73.595
</td>
<td style="text-align:center;">
74.24
</td>
<td style="text-align:center;">
39.522
</td>
<td style="text-align:center;">
53.355
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
107.52
</td>
<td style="text-align:center;">
107.517
</td>
<td style="text-align:center;">
89.988
</td>
<td style="text-align:center;">
107.517
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Standard errors in parentheses.
</td>
</tr>
</tfoot>
</table>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Treatment
</td>
<td style="text-align:center;">
13.643
</td>
<td style="text-align:center;">
13.688
</td>
<td style="text-align:center;">
-0.871
</td>
<td style="text-align:center;">
15.013
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-14.558, 41.843]
</td>
<td style="text-align:center;">
[-14.625, 42.001]
</td>
<td style="text-align:center;">
[-14.755, 13.013]
</td>
<td style="text-align:center;">
[-15.967, 45.994]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.343
</td>
<td style="text-align:center;">
0.343
</td>
<td style="text-align:center;">
0.902
</td>
<td style="text-align:center;">
0.342
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
23.067
</td>
<td style="text-align:center;">
22.994
</td>
<td style="text-align:center;">
47.568
</td>
<td style="text-align:center;">
16.578
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
48.61
</td>
<td style="text-align:center;">
48.61
</td>
<td style="text-align:center;">
106.414
</td>
<td style="text-align:center;">
48.61
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Standard errors in parentheses.
</td>
</tr>
</tfoot>
</table></li>
</ol></li>
</ol></li>
</ol></li>
</ol></li>
</ol></li>
</ol></li>
</ol></li>
</ol>
</div>
<div id="table-2-no-significant-gaps-occurred-near-the-cutoff-the-the-second-gold-medal-of-mens-judo-66kg" class="section level3 unnumbered">
<h3 class="unnumbered">Table 2: No significant gaps occurred near the
cutoff, the the second gold medal of men’s Judo 66kg</h3>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> <span class="fu">cbind</span>(df_rdd66<span class="sc">$</span>age, df_rdd66<span class="sc">$</span>female, df_rdd66<span class="sc">$</span>income, df_rdd66<span class="sc">$</span>education, df_rdd66<span class="sc">$</span>psu_rul)</span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a></span>
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a>results_judo66p <span class="ot">&lt;-</span> <span class="fu">list</span>()</span>
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a></span>
<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a>outcome_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;cabap&quot;</span>, <span class="st">&quot;ftj&quot;</span>)</span>
<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a>outcome_prefix <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;c&quot;</span>, <span class="st">&quot;f&quot;</span>)</span>
<span id="cb9-7"><a href="#cb9-7" tabindex="-1"></a></span>
<span id="cb9-8"><a href="#cb9-8" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(outcome_vars)) {</span>
<span id="cb9-9"><a href="#cb9-9" tabindex="-1"></a>  outcome_var <span class="ot">&lt;-</span> outcome_vars[i]</span>
<span id="cb9-10"><a href="#cb9-10" tabindex="-1"></a>  prefix <span class="ot">&lt;-</span> outcome_prefix[i]</span>
<span id="cb9-11"><a href="#cb9-11" tabindex="-1"></a>  </span>
<span id="cb9-12"><a href="#cb9-12" tabindex="-1"></a>  safe_rdrobust <span class="ot">&lt;-</span> <span class="cf">function</span>(..., prefix_suffix) {</span>
<span id="cb9-13"><a href="#cb9-13" tabindex="-1"></a>    <span class="fu">tryCatch</span>({</span>
<span id="cb9-14"><a href="#cb9-14" tabindex="-1"></a>      <span class="fu">rdrobust</span>(..., <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb9-15"><a href="#cb9-15" tabindex="-1"></a>    }, <span class="at">error =</span> <span class="cf">function</span>(e) {</span>
<span id="cb9-16"><a href="#cb9-16" tabindex="-1"></a>      <span class="fu">message</span>(<span class="fu">paste0</span>(<span class="st">&quot;Error in &quot;</span>, prefix_suffix, <span class="st">&quot;: &quot;</span>, e<span class="sc">$</span>message))</span>
<span id="cb9-17"><a href="#cb9-17" tabindex="-1"></a>      <span class="cn">NULL</span></span>
<span id="cb9-18"><a href="#cb9-18" tabindex="-1"></a>    })</span>
<span id="cb9-19"><a href="#cb9-19" tabindex="-1"></a>  }</span>
<span id="cb9-20"><a href="#cb9-20" tabindex="-1"></a>  </span>
<span id="cb9-21"><a href="#cb9-21" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res1&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd66[[outcome_var]], df_rdd66<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res1&quot;</span>, prefix))</span>
<span id="cb9-22"><a href="#cb9-22" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res2&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd66[[outcome_var]], df_rdd66<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;msesum&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res2&quot;</span>, prefix))</span>
<span id="cb9-23"><a href="#cb9-23" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res3&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd66[[outcome_var]], df_rdd66<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res3&quot;</span>, prefix))</span>
<span id="cb9-24"><a href="#cb9-24" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res4&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd66[[outcome_var]], df_rdd66<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;uni&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res4&quot;</span>, prefix))</span>
<span id="cb9-25"><a href="#cb9-25" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res5&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd66[[outcome_var]], df_rdd66<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;cerrd&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res5&quot;</span>, prefix))</span>
<span id="cb9-26"><a href="#cb9-26" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res6&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd66[[outcome_var]], df_rdd66<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">q =</span> <span class="dv">2</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res6&quot;</span>, prefix))</span>
<span id="cb9-27"><a href="#cb9-27" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res7&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rdd66[[outcome_var]], df_rdd66<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">p =</span> <span class="dv">2</span>, <span class="at">q =</span> <span class="dv">3</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res7&quot;</span>, prefix))</span>
<span id="cb9-28"><a href="#cb9-28" tabindex="-1"></a>}</span>
<span id="cb9-29"><a href="#cb9-29" tabindex="-1"></a></span>
<span id="cb9-30"><a href="#cb9-30" tabindex="-1"></a></span>
<span id="cb9-31"><a href="#cb9-31" tabindex="-1"></a><span class="co">#cabinet approval</span></span>
<span id="cb9-32"><a href="#cb9-32" tabindex="-1"></a></span>
<span id="cb9-33"><a href="#cb9-33" tabindex="-1"></a>final_results_list <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1c&quot;</span>, <span class="st">&quot;res2c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) {</span>
<span id="cb9-34"><a href="#cb9-34" tabindex="-1"></a>  <span class="fu">process_results</span>(results_judo66p[[res]])</span>
<span id="cb9-35"><a href="#cb9-35" tabindex="-1"></a>})</span>
<span id="cb9-36"><a href="#cb9-36" tabindex="-1"></a></span>
<span id="cb9-37"><a href="#cb9-37" tabindex="-1"></a>final_combined_results <span class="ot">&lt;-</span> <span class="fu">do.call</span>(cbind, <span class="fu">lapply</span>(final_results_list, <span class="cf">function</span>(x) x[, <span class="dv">2</span>]))</span>
<span id="cb9-38"><a href="#cb9-38" tabindex="-1"></a></span>
<span id="cb9-39"><a href="#cb9-39" tabindex="-1"></a>kernel_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1c&quot;</span>, <span class="st">&quot;res2c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) results_judo66p[[res]]<span class="sc">$</span>kernel)</span>
<span id="cb9-40"><a href="#cb9-40" tabindex="-1"></a>bwselect_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1c&quot;</span>,<span class="st">&quot;res2c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) results_judo66p[[res]]<span class="sc">$</span>bwselect)</span>
<span id="cb9-41"><a href="#cb9-41" tabindex="-1"></a>bwp_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1c&quot;</span>,<span class="st">&quot;res2c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) <span class="fu">round</span>(results_judo66p[[res]]<span class="sc">$</span>bws[<span class="dv">1</span>,<span class="dv">1</span>], <span class="dv">3</span>))</span>
<span id="cb9-42"><a href="#cb9-42" tabindex="-1"></a>bwb_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1c&quot;</span>,<span class="st">&quot;res2c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) <span class="fu">round</span>(results_judo66p[[res]]<span class="sc">$</span>bws[<span class="dv">2</span>,<span class="dv">1</span>], <span class="dv">3</span>))</span>
<span id="cb9-43"><a href="#cb9-43" tabindex="-1"></a>pol_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1c&quot;</span>,<span class="st">&quot;res2c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) results_judo66p[[res]]<span class="sc">$</span>p)</span>
<span id="cb9-44"><a href="#cb9-44" tabindex="-1"></a>q_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1c&quot;</span>,<span class="st">&quot;res2c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) results_judo66p[[res]]<span class="sc">$</span>q)</span>
<span id="cb9-45"><a href="#cb9-45" tabindex="-1"></a>n_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1c&quot;</span>,<span class="st">&quot;res2c&quot;</span>, <span class="st">&quot;res4c&quot;</span>, <span class="st">&quot;res5c&quot;</span>), <span class="cf">function</span>(res) <span class="fu">sum</span>(results_judo66p[[res]]<span class="sc">$</span>N))</span>
<span id="cb9-46"><a href="#cb9-46" tabindex="-1"></a></span>
<span id="cb9-47"><a href="#cb9-47" tabindex="-1"></a>final_combined_results_with_kernel <span class="ot">&lt;-</span> <span class="fu">rbind</span>(</span>
<span id="cb9-48"><a href="#cb9-48" tabindex="-1"></a>  final_combined_results,</span>
<span id="cb9-49"><a href="#cb9-49" tabindex="-1"></a>  n_results,</span>
<span id="cb9-50"><a href="#cb9-50" tabindex="-1"></a>  kernel_results,</span>
<span id="cb9-51"><a href="#cb9-51" tabindex="-1"></a>  bwselect_results,</span>
<span id="cb9-52"><a href="#cb9-52" tabindex="-1"></a>  pol_results,</span>
<span id="cb9-53"><a href="#cb9-53" tabindex="-1"></a>  q_results,</span>
<span id="cb9-54"><a href="#cb9-54" tabindex="-1"></a>  bwp_results,</span>
<span id="cb9-55"><a href="#cb9-55" tabindex="-1"></a>  bwb_results</span>
<span id="cb9-56"><a href="#cb9-56" tabindex="-1"></a>)</span>
<span id="cb9-57"><a href="#cb9-57" tabindex="-1"></a></span>
<span id="cb9-58"><a href="#cb9-58" tabindex="-1"></a>df_rdd66_judo66p <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="fu">c</span>(<span class="st">&quot;Treatment&quot;</span>, <span class="st">&quot;Robust 95% CI&quot;</span>,<span class="st">&quot;Robust $p$-value&quot;</span>, <span class="st">&quot;Observations&quot;</span> , <span class="st">&quot;Kernel type&quot;</span>,</span>
<span id="cb9-59"><a href="#cb9-59" tabindex="-1"></a>                                 <span class="st">&quot;BW type&quot;</span>, <span class="st">&quot;Order loc. poly.($p$)&quot;</span>, <span class="st">&quot;Order bias ($q$)&quot;</span>, <span class="st">&quot;BW loc. poly.($h$)&quot;</span>,</span>
<span id="cb9-60"><a href="#cb9-60" tabindex="-1"></a>                                 <span class="st">&quot;BW bias ($b$)&quot;</span>), final_combined_results_with_kernel)</span>
<span id="cb9-61"><a href="#cb9-61" tabindex="-1"></a></span>
<span id="cb9-62"><a href="#cb9-62" tabindex="-1"></a><span class="fu">colnames</span>(df_rdd66_judo66p) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;&quot;</span>, <span class="st">&quot;(1)&quot;</span>, <span class="st">&quot;(2)&quot;</span>, <span class="st">&quot;(3)&quot;</span>, <span class="st">&quot;(4)&quot;</span>)</span>
<span id="cb9-63"><a href="#cb9-63" tabindex="-1"></a></span>
<span id="cb9-64"><a href="#cb9-64" tabindex="-1"></a></span>
<span id="cb9-65"><a href="#cb9-65" tabindex="-1"></a></span>
<span id="cb9-66"><a href="#cb9-66" tabindex="-1"></a></span>
<span id="cb9-67"><a href="#cb9-67" tabindex="-1"></a><span class="co">#feeling thermometer</span></span>
<span id="cb9-68"><a href="#cb9-68" tabindex="-1"></a>final_results_list <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) {</span>
<span id="cb9-69"><a href="#cb9-69" tabindex="-1"></a>  <span class="fu">process_results</span>(results_judo66p[[res]])</span>
<span id="cb9-70"><a href="#cb9-70" tabindex="-1"></a>})</span>
<span id="cb9-71"><a href="#cb9-71" tabindex="-1"></a></span>
<span id="cb9-72"><a href="#cb9-72" tabindex="-1"></a>final_combined_results <span class="ot">&lt;-</span> <span class="fu">do.call</span>(cbind, <span class="fu">lapply</span>(final_results_list, <span class="cf">function</span>(x) x[, <span class="dv">2</span>]))</span>
<span id="cb9-73"><a href="#cb9-73" tabindex="-1"></a></span>
<span id="cb9-74"><a href="#cb9-74" tabindex="-1"></a>kernel_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) results_judo66p[[res]]<span class="sc">$</span>kernel)</span>
<span id="cb9-75"><a href="#cb9-75" tabindex="-1"></a>bwselect_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) results_judo66p[[res]]<span class="sc">$</span>bwselect)</span>
<span id="cb9-76"><a href="#cb9-76" tabindex="-1"></a>bwp_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) <span class="fu">round</span>(results_judo66p[[res]]<span class="sc">$</span>bws[<span class="dv">1</span>,<span class="dv">1</span>], <span class="dv">3</span>))</span>
<span id="cb9-77"><a href="#cb9-77" tabindex="-1"></a>bwb_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) <span class="fu">round</span>(results_judo66p[[res]]<span class="sc">$</span>bws[<span class="dv">2</span>,<span class="dv">1</span>], <span class="dv">3</span>))</span>
<span id="cb9-78"><a href="#cb9-78" tabindex="-1"></a>pol_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) results_judo66p[[res]]<span class="sc">$</span>p)</span>
<span id="cb9-79"><a href="#cb9-79" tabindex="-1"></a>q_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) results_judo66p[[res]]<span class="sc">$</span>q)</span>
<span id="cb9-80"><a href="#cb9-80" tabindex="-1"></a>n_results <span class="ot">&lt;-</span> <span class="fu">sapply</span>(<span class="fu">c</span>(<span class="st">&quot;res1f&quot;</span>,  <span class="st">&quot;res2f&quot;</span>, <span class="st">&quot;res4f&quot;</span>, <span class="st">&quot;res5f&quot;</span>), <span class="cf">function</span>(res) <span class="fu">sum</span>(results_judo66p[[res]]<span class="sc">$</span>N))</span>
<span id="cb9-81"><a href="#cb9-81" tabindex="-1"></a></span>
<span id="cb9-82"><a href="#cb9-82" tabindex="-1"></a>final_combined_results_with_kernel <span class="ot">&lt;-</span> <span class="fu">rbind</span>(</span>
<span id="cb9-83"><a href="#cb9-83" tabindex="-1"></a>  final_combined_results,</span>
<span id="cb9-84"><a href="#cb9-84" tabindex="-1"></a>  n_results,</span>
<span id="cb9-85"><a href="#cb9-85" tabindex="-1"></a>  kernel_results,</span>
<span id="cb9-86"><a href="#cb9-86" tabindex="-1"></a>  bwselect_results,</span>
<span id="cb9-87"><a href="#cb9-87" tabindex="-1"></a>  pol_results,</span>
<span id="cb9-88"><a href="#cb9-88" tabindex="-1"></a>  q_results,</span>
<span id="cb9-89"><a href="#cb9-89" tabindex="-1"></a>  bwp_results,</span>
<span id="cb9-90"><a href="#cb9-90" tabindex="-1"></a>  bwb_results</span>
<span id="cb9-91"><a href="#cb9-91" tabindex="-1"></a>)</span>
<span id="cb9-92"><a href="#cb9-92" tabindex="-1"></a></span>
<span id="cb9-93"><a href="#cb9-93" tabindex="-1"></a>df_rdd66_judo66pft <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="fu">c</span>(<span class="st">&quot;Treatment&quot;</span>, <span class="st">&quot;Robust 95% CI&quot;</span>,<span class="st">&quot;Robust $p$-value&quot;</span>, <span class="st">&quot;Observations&quot;</span> , <span class="st">&quot;Kernel type&quot;</span>,</span>
<span id="cb9-94"><a href="#cb9-94" tabindex="-1"></a>                                   <span class="st">&quot;BW type&quot;</span>, <span class="st">&quot;Order loc. poly.($p$)&quot;</span>, <span class="st">&quot;Order bias ($q$)&quot;</span>, <span class="st">&quot;BW loc. poly.($h$)&quot;</span>,</span>
<span id="cb9-95"><a href="#cb9-95" tabindex="-1"></a>                                   <span class="st">&quot;BW bias ($b$)&quot;</span>), final_combined_results_with_kernel)</span>
<span id="cb9-96"><a href="#cb9-96" tabindex="-1"></a></span>
<span id="cb9-97"><a href="#cb9-97" tabindex="-1"></a><span class="fu">colnames</span>(df_rdd66_judo66pft) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;&quot;</span>, <span class="st">&quot;(1)&quot;</span>, <span class="st">&quot;(2)&quot;</span>, <span class="st">&quot;(3)&quot;</span>, <span class="st">&quot;(4)&quot;</span>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Treatment
</td>
<td style="text-align:center;">
0.305
</td>
<td style="text-align:center;">
0.288
</td>
<td style="text-align:center;">
-3.887
</td>
<td style="text-align:center;">
0.261
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[0.044, 0.565]
</td>
<td style="text-align:center;">
[0.032, 0.543]
</td>
<td style="text-align:center;">
[-11.767, 3.993]
</td>
<td style="text-align:center;">
[-0.002, 0.523]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.022
</td>
<td style="text-align:center;">
0.027
</td>
<td style="text-align:center;">
0.334
</td>
<td style="text-align:center;">
0.051
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
2.38
</td>
<td style="text-align:center;">
2.658
</td>
<td style="text-align:center;">
1.338
</td>
<td style="text-align:center;">
1.714
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
4.017
</td>
<td style="text-align:center;">
4.176
</td>
<td style="text-align:center;">
3.275
</td>
<td style="text-align:center;">
4.017
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Standard errors in parentheses.
</td>
</tr>
</tfoot>
</table>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Treatment
</td>
<td style="text-align:center;">
-4.296
</td>
<td style="text-align:center;">
-4.290
</td>
<td style="text-align:center;">
290.282
</td>
<td style="text-align:center;">
-4.890
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-17.248, 8.656]
</td>
<td style="text-align:center;">
[-17.168, 8.589]
</td>
<td style="text-align:center;">
[-434.616, 1015.181]
</td>
<td style="text-align:center;">
[-19.811, 10.030]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.516
</td>
<td style="text-align:center;">
0.514
</td>
<td style="text-align:center;">
0.433
</td>
<td style="text-align:center;">
0.521
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
4.127
</td>
<td style="text-align:center;">
4.202
</td>
<td style="text-align:center;">
1.656
</td>
<td style="text-align:center;">
2.972
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
5.91
</td>
<td style="text-align:center;">
5.922
</td>
<td style="text-align:center;">
3.335
</td>
<td style="text-align:center;">
5.91
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Standard errors in parentheses.
</td>
</tr>
</tfoot>
</table></li>
</ol></li>
</ol></li>
</ol></li>
</ol></li>
</ol></li>
</ol></li>
</ol></li>
</ol>
</div>
</div>
<div id="equivalence-test" class="section level2 unnumbered">
<h2 class="unnumbered">Equivalence test</h2>
<div id="figure-4-across-bin-widths-the-first-gold-medal-satisfies-cross-sectional-equivalence-at-0.2-sd-while-time-bin-equivalence-emerges-only-with-larger-k-and-narrow-bins-indicating-a-practically-negligible-effect-on-cabinet-approval" class="section level3 unnumbered">
<h3 class="unnumbered">Figure 4: Across bin widths, the first gold medal
satisfies cross-sectional equivalence at ±0.2 SD, while time-bin
equivalence emerges only with larger k and narrow bins, indicating a
practically negligible effect on cabinet approval</h3>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a><span class="cf">if</span> (<span class="fu">requireNamespace</span>(<span class="st">&quot;conflicted&quot;</span>, <span class="at">quietly =</span> <span class="cn">TRUE</span>)) {</span>
<span id="cb10-2"><a href="#cb10-2" tabindex="-1"></a>  conflicted<span class="sc">::</span><span class="fu">conflict_prefer</span>(<span class="st">&quot;select&quot;</span>, <span class="st">&quot;dplyr&quot;</span>)</span>
<span id="cb10-3"><a href="#cb10-3" tabindex="-1"></a>  conflicted<span class="sc">::</span><span class="fu">conflict_prefer</span>(<span class="st">&quot;filter&quot;</span>, <span class="st">&quot;dplyr&quot;</span>)</span>
<span id="cb10-4"><a href="#cb10-4" tabindex="-1"></a>  conflicted<span class="sc">::</span><span class="fu">conflict_prefer</span>(<span class="st">&quot;lag&quot;</span>,    <span class="st">&quot;dplyr&quot;</span>)</span>
<span id="cb10-5"><a href="#cb10-5" tabindex="-1"></a>}</span>
<span id="cb10-6"><a href="#cb10-6" tabindex="-1"></a></span>
<span id="cb10-7"><a href="#cb10-7" tabindex="-1"></a>fmt <span class="ot">&lt;-</span> <span class="cf">function</span>(x, <span class="at">d =</span> <span class="dv">3</span>) <span class="fu">ifelse</span>(<span class="fu">is.finite</span>(x), <span class="fu">formatC</span>(x, <span class="at">format =</span> <span class="st">&quot;f&quot;</span>, <span class="at">digits =</span> d), <span class="st">&quot;NA&quot;</span>)</span>
<span id="cb10-8"><a href="#cb10-8" tabindex="-1"></a></span>
<span id="cb10-9"><a href="#cb10-9" tabindex="-1"></a>run_tost <span class="ot">&lt;-</span> <span class="cf">function</span>(x, y, eps, <span class="at">conf.level =</span> <span class="fl">0.95</span>) {</span>
<span id="cb10-10"><a href="#cb10-10" tabindex="-1"></a>  x <span class="ot">&lt;-</span> x[<span class="fu">is.finite</span>(x)]; y <span class="ot">&lt;-</span> y[<span class="fu">is.finite</span>(y)]</span>
<span id="cb10-11"><a href="#cb10-11" tabindex="-1"></a>  eff <span class="ot">&lt;-</span> <span class="cf">if</span> (<span class="fu">length</span>(x) <span class="sc">&gt;</span> <span class="dv">0</span> <span class="sc">&amp;&amp;</span> <span class="fu">length</span>(y) <span class="sc">&gt;</span> <span class="dv">0</span>) <span class="fu">mean</span>(x) <span class="sc">-</span> <span class="fu">mean</span>(y) <span class="cf">else</span> <span class="cn">NA_real_</span></span>
<span id="cb10-12"><a href="#cb10-12" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">length</span>(x) <span class="sc">&lt;</span> <span class="dv">2</span><span class="dt">L</span> <span class="sc">||</span> <span class="fu">length</span>(y) <span class="sc">&lt;</span> <span class="dv">2</span><span class="dt">L</span>)</span>
<span id="cb10-13"><a href="#cb10-13" tabindex="-1"></a>    <span class="fu">return</span>(<span class="fu">list</span>(<span class="at">eff =</span> eff, <span class="at">ci90 =</span> <span class="fu">c</span>(<span class="cn">NA_real_</span>, <span class="cn">NA_real_</span>), <span class="at">equiv =</span> <span class="cn">NA</span>))</span>
<span id="cb10-14"><a href="#cb10-14" tabindex="-1"></a>  out <span class="ot">&lt;-</span> <span class="fu">tryCatch</span>(equivalence<span class="sc">::</span><span class="fu">tost</span>(x, y, <span class="at">epsilon =</span> eps, <span class="at">conf.level =</span> conf.level),</span>
<span id="cb10-15"><a href="#cb10-15" tabindex="-1"></a>                  <span class="at">error =</span> <span class="cf">function</span>(e) <span class="cn">NULL</span>)</span>
<span id="cb10-16"><a href="#cb10-16" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">is.null</span>(out)) <span class="fu">return</span>(<span class="fu">list</span>(<span class="at">eff =</span> eff, <span class="at">ci90 =</span> <span class="fu">c</span>(<span class="cn">NA_real_</span>, <span class="cn">NA_real_</span>), <span class="at">equiv =</span> <span class="cn">NA</span>))</span>
<span id="cb10-17"><a href="#cb10-17" tabindex="-1"></a>  ci90 <span class="ot">&lt;-</span> <span class="fu">tryCatch</span>(<span class="fu">as.numeric</span>(out<span class="sc">$</span>tost.interval), <span class="at">error =</span> <span class="cf">function</span>(e) <span class="fu">c</span>(<span class="cn">NA_real_</span>, <span class="cn">NA_real_</span>))</span>
<span id="cb10-18"><a href="#cb10-18" tabindex="-1"></a>  equiv_flag <span class="ot">&lt;-</span> <span class="cf">if</span> (<span class="fu">all</span>(<span class="fu">is.finite</span>(ci90))) (ci90[<span class="dv">1</span>] <span class="sc">&gt;</span> <span class="sc">-</span>eps) <span class="sc">&amp;&amp;</span> (ci90[<span class="dv">2</span>] <span class="sc">&lt;</span> eps) <span class="cf">else</span> <span class="cn">NA</span></span>
<span id="cb10-19"><a href="#cb10-19" tabindex="-1"></a>  <span class="fu">list</span>(<span class="at">eff =</span> eff, <span class="at">ci90 =</span> ci90, <span class="at">equiv =</span> equiv_flag)</span>
<span id="cb10-20"><a href="#cb10-20" tabindex="-1"></a>}</span>
<span id="cb10-21"><a href="#cb10-21" tabindex="-1"></a></span>
<span id="cb10-22"><a href="#cb10-22" tabindex="-1"></a>choose_minutes <span class="ot">&lt;-</span> <span class="cf">function</span>(df, time_col, <span class="at">unit =</span> <span class="fu">c</span>(<span class="st">&quot;mins&quot;</span>, <span class="st">&quot;secs&quot;</span>, <span class="st">&quot;hours&quot;</span>, <span class="st">&quot;auto&quot;</span>)) {</span>
<span id="cb10-23"><a href="#cb10-23" tabindex="-1"></a>  unit <span class="ot">&lt;-</span> <span class="fu">match.arg</span>(unit); t <span class="ot">&lt;-</span> df[[time_col]]</span>
<span id="cb10-24"><a href="#cb10-24" tabindex="-1"></a>  num_to_min <span class="ot">&lt;-</span> <span class="cf">function</span>(x) {</span>
<span id="cb10-25"><a href="#cb10-25" tabindex="-1"></a>    <span class="cf">if</span> (unit <span class="sc">==</span> <span class="st">&quot;mins&quot;</span>)  <span class="fu">return</span>(<span class="fu">as.numeric</span>(x))</span>
<span id="cb10-26"><a href="#cb10-26" tabindex="-1"></a>    <span class="cf">if</span> (unit <span class="sc">==</span> <span class="st">&quot;secs&quot;</span>)  <span class="fu">return</span>(<span class="fu">as.numeric</span>(x) <span class="sc">/</span> <span class="dv">60</span>)</span>
<span id="cb10-27"><a href="#cb10-27" tabindex="-1"></a>    <span class="cf">if</span> (unit <span class="sc">==</span> <span class="st">&quot;hours&quot;</span>) <span class="fu">return</span>(<span class="fu">as.numeric</span>(x) <span class="sc">*</span> <span class="dv">60</span>)</span>
<span id="cb10-28"><a href="#cb10-28" tabindex="-1"></a>    x <span class="ot">&lt;-</span> <span class="fu">as.numeric</span>(x); <span class="cf">if</span> (<span class="fu">max</span>(x, <span class="at">na.rm =</span> <span class="cn">TRUE</span>) <span class="sc">&gt;</span> <span class="dv">180</span>) x<span class="sc">/</span><span class="dv">60</span> <span class="cf">else</span> x</span>
<span id="cb10-29"><a href="#cb10-29" tabindex="-1"></a>  }</span>
<span id="cb10-30"><a href="#cb10-30" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">inherits</span>(t, <span class="st">&quot;POSIXt&quot;</span>)) {</span>
<span id="cb10-31"><a href="#cb10-31" tabindex="-1"></a>    t0 <span class="ot">&lt;-</span> <span class="fu">min</span>(t, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb10-32"><a href="#cb10-32" tabindex="-1"></a>    <span class="fu">as.numeric</span>(<span class="fu">difftime</span>(t, t0, <span class="at">units =</span> <span class="st">&quot;mins&quot;</span>))</span>
<span id="cb10-33"><a href="#cb10-33" tabindex="-1"></a>  } <span class="cf">else</span> <span class="cf">if</span> (<span class="fu">inherits</span>(t, <span class="st">&quot;Date&quot;</span>)) {</span>
<span id="cb10-34"><a href="#cb10-34" tabindex="-1"></a>    <span class="fu">as.numeric</span>(t <span class="sc">-</span> <span class="fu">min</span>(t, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)) <span class="sc">*</span> <span class="dv">24</span> <span class="sc">*</span> <span class="dv">60</span></span>
<span id="cb10-35"><a href="#cb10-35" tabindex="-1"></a>  } <span class="cf">else</span> <span class="cf">if</span> (<span class="fu">is.numeric</span>(t)) {</span>
<span id="cb10-36"><a href="#cb10-36" tabindex="-1"></a>    <span class="fu">num_to_min</span>(t)</span>
<span id="cb10-37"><a href="#cb10-37" tabindex="-1"></a>  } <span class="cf">else</span> <span class="fu">stop</span>(<span class="st">&quot;time_col must be POSIXct/Date/numeric.&quot;</span>)</span>
<span id="cb10-38"><a href="#cb10-38" tabindex="-1"></a>}</span>
<span id="cb10-39"><a href="#cb10-39" tabindex="-1"></a></span>
<span id="cb10-40"><a href="#cb10-40" tabindex="-1"></a>eps_timebin <span class="ot">&lt;-</span> <span class="cf">function</span>(df, outcome, time_col, <span class="at">bin_minutes =</span> <span class="dv">60</span>, <span class="at">k =</span> <span class="fl">0.30</span>, <span class="at">unit =</span> <span class="st">&quot;mins&quot;</span>) {</span>
<span id="cb10-41"><a href="#cb10-41" tabindex="-1"></a>  mins <span class="ot">&lt;-</span> <span class="fu">choose_minutes</span>(df, time_col, unit)</span>
<span id="cb10-42"><a href="#cb10-42" tabindex="-1"></a>  tmp <span class="ot">&lt;-</span> df; tmp<span class="sc">$</span>.__bin__ <span class="ot">&lt;-</span> <span class="fu">floor</span>(mins <span class="sc">/</span> bin_minutes)</span>
<span id="cb10-43"><a href="#cb10-43" tabindex="-1"></a>  bm <span class="ot">&lt;-</span> tmp <span class="sc">%&gt;%</span></span>
<span id="cb10-44"><a href="#cb10-44" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">group_by</span>(.__bin__) <span class="sc">%&gt;%</span></span>
<span id="cb10-45"><a href="#cb10-45" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">summarise</span>(<span class="at">mu =</span> <span class="fu">mean</span>(.data[[outcome]], <span class="at">na.rm =</span> <span class="cn">TRUE</span>), <span class="at">.groups =</span> <span class="st">&quot;drop&quot;</span>)</span>
<span id="cb10-46"><a href="#cb10-46" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">nrow</span>(bm) <span class="sc">&lt;</span> <span class="dv">2</span><span class="dt">L</span>) <span class="fu">return</span>(<span class="cn">NA_real_</span>)</span>
<span id="cb10-47"><a href="#cb10-47" tabindex="-1"></a>  k <span class="sc">*</span> stats<span class="sc">::</span><span class="fu">sd</span>(bm<span class="sc">$</span>mu, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb10-48"><a href="#cb10-48" tabindex="-1"></a>}</span>
<span id="cb10-49"><a href="#cb10-49" tabindex="-1"></a></span>
<span id="cb10-50"><a href="#cb10-50" tabindex="-1"></a>eps_crosssec <span class="ot">&lt;-</span> <span class="cf">function</span>(df, outcome, <span class="at">k =</span> <span class="fl">0.30</span>, <span class="at">mode =</span> <span class="fu">c</span>(<span class="st">&quot;pooled&quot;</span>, <span class="st">&quot;overall&quot;</span>)) {</span>
<span id="cb10-51"><a href="#cb10-51" tabindex="-1"></a>  mode <span class="ot">&lt;-</span> <span class="fu">match.arg</span>(mode); z <span class="ot">&lt;-</span> df[[outcome]]</span>
<span id="cb10-52"><a href="#cb10-52" tabindex="-1"></a>  <span class="cf">if</span> (mode <span class="sc">==</span> <span class="st">&quot;pooled&quot;</span>) {</span>
<span id="cb10-53"><a href="#cb10-53" tabindex="-1"></a>    z1 <span class="ot">&lt;-</span> z[df<span class="sc">$</span>cutoff <span class="sc">==</span> <span class="dv">1</span>]; z0 <span class="ot">&lt;-</span> z[df<span class="sc">$</span>cutoff <span class="sc">==</span> <span class="dv">0</span>]</span>
<span id="cb10-54"><a href="#cb10-54" tabindex="-1"></a>    s1 <span class="ot">&lt;-</span> stats<span class="sc">::</span><span class="fu">sd</span>(z1, <span class="at">na.rm =</span> <span class="cn">TRUE</span>); s0 <span class="ot">&lt;-</span> stats<span class="sc">::</span><span class="fu">sd</span>(z0, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb10-55"><a href="#cb10-55" tabindex="-1"></a>    n1 <span class="ot">&lt;-</span> <span class="fu">sum</span>(<span class="fu">is.finite</span>(z1));          n0 <span class="ot">&lt;-</span> <span class="fu">sum</span>(<span class="fu">is.finite</span>(z0))</span>
<span id="cb10-56"><a href="#cb10-56" tabindex="-1"></a>    s  <span class="ot">&lt;-</span> <span class="fu">sqrt</span>(((n1 <span class="sc">-</span> <span class="dv">1</span>) <span class="sc">*</span> s1<span class="sc">^</span><span class="dv">2</span> <span class="sc">+</span> (n0 <span class="sc">-</span> <span class="dv">1</span>) <span class="sc">*</span> s0<span class="sc">^</span><span class="dv">2</span>) <span class="sc">/</span> (n1 <span class="sc">+</span> n0 <span class="sc">-</span> <span class="dv">2</span>))</span>
<span id="cb10-57"><a href="#cb10-57" tabindex="-1"></a>  } <span class="cf">else</span> {</span>
<span id="cb10-58"><a href="#cb10-58" tabindex="-1"></a>    s  <span class="ot">&lt;-</span> stats<span class="sc">::</span><span class="fu">sd</span>(z, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb10-59"><a href="#cb10-59" tabindex="-1"></a>  }</span>
<span id="cb10-60"><a href="#cb10-60" tabindex="-1"></a>  k <span class="sc">*</span> s</span>
<span id="cb10-61"><a href="#cb10-61" tabindex="-1"></a>}</span>
<span id="cb10-62"><a href="#cb10-62" tabindex="-1"></a></span>
<span id="cb10-63"><a href="#cb10-63" tabindex="-1"></a>common_ci90 <span class="ot">&lt;-</span> <span class="cf">function</span>(df, outcome) {</span>
<span id="cb10-64"><a href="#cb10-64" tabindex="-1"></a>  x <span class="ot">&lt;-</span> df[[outcome]][df<span class="sc">$</span>cutoff <span class="sc">==</span> <span class="dv">1</span>]; y <span class="ot">&lt;-</span> df[[outcome]][df<span class="sc">$</span>cutoff <span class="sc">==</span> <span class="dv">0</span>]</span>
<span id="cb10-65"><a href="#cb10-65" tabindex="-1"></a>  ci <span class="ot">&lt;-</span> <span class="fu">tryCatch</span>(stats<span class="sc">::</span><span class="fu">t.test</span>(x, y, <span class="at">conf.level =</span> <span class="fl">0.90</span>, <span class="at">var.equal =</span> <span class="cn">FALSE</span>)<span class="sc">$</span>conf.int,</span>
<span id="cb10-66"><a href="#cb10-66" tabindex="-1"></a>                 <span class="at">error =</span> <span class="cf">function</span>(e) <span class="fu">c</span>(<span class="cn">NA_real_</span>, <span class="cn">NA_real_</span>))</span>
<span id="cb10-67"><a href="#cb10-67" tabindex="-1"></a>  <span class="fu">c</span>(ci[<span class="dv">1</span>], ci[<span class="dv">2</span>])</span>
<span id="cb10-68"><a href="#cb10-68" tabindex="-1"></a>}</span>
<span id="cb10-69"><a href="#cb10-69" tabindex="-1"></a></span>
<span id="cb10-70"><a href="#cb10-70" tabindex="-1"></a>build_overlap_singlecs <span class="ot">&lt;-</span> <span class="cf">function</span>(df, outcome, time_col,</span>
<span id="cb10-71"><a href="#cb10-71" tabindex="-1"></a>                                   <span class="at">bin_set   =</span> <span class="fu">seq</span>(<span class="dv">10</span>, <span class="dv">120</span>, <span class="at">by =</span> <span class="dv">10</span>),</span>
<span id="cb10-72"><a href="#cb10-72" tabindex="-1"></a>                                   <span class="at">k_set     =</span> <span class="fu">c</span>(<span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span>, <span class="fl">1.0</span>),</span>
<span id="cb10-73"><a href="#cb10-73" tabindex="-1"></a>                                   <span class="at">cs_mode   =</span> <span class="fu">c</span>(<span class="st">&quot;pooled&quot;</span>, <span class="st">&quot;overall&quot;</span>),</span>
<span id="cb10-74"><a href="#cb10-74" tabindex="-1"></a>                                   <span class="at">unit_time =</span> <span class="st">&quot;mins&quot;</span>,</span>
<span id="cb10-75"><a href="#cb10-75" tabindex="-1"></a>                                   <span class="at">label_digits =</span> <span class="dv">2</span>) {</span>
<span id="cb10-76"><a href="#cb10-76" tabindex="-1"></a></span>
<span id="cb10-77"><a href="#cb10-77" tabindex="-1"></a>  cs_mode <span class="ot">&lt;-</span> <span class="fu">match.arg</span>(cs_mode)</span>
<span id="cb10-78"><a href="#cb10-78" tabindex="-1"></a>  <span class="fu">stopifnot</span>(<span class="fu">all</span>(<span class="fu">c</span>(outcome, <span class="st">&quot;cutoff&quot;</span>, time_col) <span class="sc">%in%</span> <span class="fu">names</span>(df)))</span>
<span id="cb10-79"><a href="#cb10-79" tabindex="-1"></a></span>
<span id="cb10-80"><a href="#cb10-80" tabindex="-1"></a>  x <span class="ot">&lt;-</span> df[[outcome]][df<span class="sc">$</span>cutoff <span class="sc">==</span> <span class="dv">1</span>]</span>
<span id="cb10-81"><a href="#cb10-81" tabindex="-1"></a>  y <span class="ot">&lt;-</span> df[[outcome]][df<span class="sc">$</span>cutoff <span class="sc">==</span> <span class="dv">0</span>]</span>
<span id="cb10-82"><a href="#cb10-82" tabindex="-1"></a></span>
<span id="cb10-83"><a href="#cb10-83" tabindex="-1"></a>  cs_tab <span class="ot">&lt;-</span> tibble<span class="sc">::</span><span class="fu">tibble</span>(<span class="at">k =</span> k_set) <span class="sc">%&gt;%</span></span>
<span id="cb10-84"><a href="#cb10-84" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb10-85"><a href="#cb10-85" tabindex="-1"></a>      <span class="at">eps_cs =</span> purrr<span class="sc">::</span><span class="fu">map_dbl</span>(k, <span class="sc">~</span> <span class="fu">eps_crosssec</span>(df, outcome, <span class="at">k =</span> .x, <span class="at">mode =</span> cs_mode)),</span>
<span id="cb10-86"><a href="#cb10-86" tabindex="-1"></a>      <span class="at">res_cs =</span> purrr<span class="sc">::</span><span class="fu">map</span>(eps_cs, <span class="sc">~</span> <span class="fu">run_tost</span>(x, y, <span class="at">eps =</span> .x)),</span>
<span id="cb10-87"><a href="#cb10-87" tabindex="-1"></a>      <span class="at">cs_eq  =</span> purrr<span class="sc">::</span><span class="fu">map_lgl</span>(res_cs, <span class="sc">~</span> .x<span class="sc">$</span>equiv <span class="sc">%in%</span> <span class="cn">TRUE</span>)</span>
<span id="cb10-88"><a href="#cb10-88" tabindex="-1"></a>    )</span>
<span id="cb10-89"><a href="#cb10-89" tabindex="-1"></a></span>
<span id="cb10-90"><a href="#cb10-90" tabindex="-1"></a>  tb_tab <span class="ot">&lt;-</span> tidyr<span class="sc">::</span><span class="fu">crossing</span>(<span class="at">bin_minutes =</span> bin_set, <span class="at">k =</span> k_set) <span class="sc">%&gt;%</span></span>
<span id="cb10-91"><a href="#cb10-91" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb10-92"><a href="#cb10-92" tabindex="-1"></a>      <span class="at">eps_tb =</span> purrr<span class="sc">::</span><span class="fu">pmap_dbl</span>(</span>
<span id="cb10-93"><a href="#cb10-93" tabindex="-1"></a>        <span class="fu">list</span>(bin_minutes, k),</span>
<span id="cb10-94"><a href="#cb10-94" tabindex="-1"></a>        <span class="sc">~</span> <span class="fu">eps_timebin</span>(df, outcome, time_col, <span class="at">bin_minutes =</span> ..<span class="dv">1</span>, <span class="at">k =</span> ..<span class="dv">2</span>, <span class="at">unit =</span> unit_time)</span>
<span id="cb10-95"><a href="#cb10-95" tabindex="-1"></a>      ),</span>
<span id="cb10-96"><a href="#cb10-96" tabindex="-1"></a>      <span class="at">res_tb =</span> purrr<span class="sc">::</span><span class="fu">map</span>(eps_tb, <span class="sc">~</span> <span class="fu">run_tost</span>(x, y, <span class="at">eps =</span> .x)),</span>
<span id="cb10-97"><a href="#cb10-97" tabindex="-1"></a>      <span class="at">tb_eq  =</span> purrr<span class="sc">::</span><span class="fu">map_lgl</span>(res_tb, <span class="sc">~</span> .x<span class="sc">$</span>equiv <span class="sc">%in%</span> <span class="cn">TRUE</span>)</span>
<span id="cb10-98"><a href="#cb10-98" tabindex="-1"></a>    )</span>
<span id="cb10-99"><a href="#cb10-99" tabindex="-1"></a></span>
<span id="cb10-100"><a href="#cb10-100" tabindex="-1"></a>  out <span class="ot">&lt;-</span> tb_tab <span class="sc">%&gt;%</span></span>
<span id="cb10-101"><a href="#cb10-101" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">left_join</span>(cs_tab, <span class="at">by =</span> <span class="st">&quot;k&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb10-102"><a href="#cb10-102" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb10-103"><a href="#cb10-103" tabindex="-1"></a>      <span class="at">category =</span> dplyr<span class="sc">::</span><span class="fu">case_when</span>(</span>
<span id="cb10-104"><a href="#cb10-104" tabindex="-1"></a>        tb_eq <span class="sc">&amp;</span> cs_eq  <span class="sc">~</span> <span class="st">&quot;Both&quot;</span>,</span>
<span id="cb10-105"><a href="#cb10-105" tabindex="-1"></a>        tb_eq <span class="sc">&amp;</span> <span class="sc">!</span>cs_eq <span class="sc">~</span> <span class="st">&quot;TB only&quot;</span>,</span>
<span id="cb10-106"><a href="#cb10-106" tabindex="-1"></a>        <span class="sc">!</span>tb_eq <span class="sc">&amp;</span> cs_eq <span class="sc">~</span> <span class="st">&quot;CS only&quot;</span>,</span>
<span id="cb10-107"><a href="#cb10-107" tabindex="-1"></a>        <span class="cn">TRUE</span>           <span class="sc">~</span> <span class="st">&quot;Neither&quot;</span></span>
<span id="cb10-108"><a href="#cb10-108" tabindex="-1"></a>      ),</span>
<span id="cb10-109"><a href="#cb10-109" tabindex="-1"></a>      <span class="at">k_f   =</span> <span class="fu">factor</span>(k, <span class="at">levels =</span> <span class="fu">c</span>(<span class="fl">1.0</span>, <span class="fl">0.5</span>, <span class="fl">0.3</span>, <span class="fl">0.2</span>)),</span>
<span id="cb10-110"><a href="#cb10-110" tabindex="-1"></a>      <span class="at">bin_f =</span> <span class="fu">factor</span>(bin_minutes, <span class="at">levels =</span> bin_set),</span>
<span id="cb10-111"><a href="#cb10-111" tabindex="-1"></a></span>
<span id="cb10-112"><a href="#cb10-112" tabindex="-1"></a>      <span class="at">EB_TB_low  =</span> <span class="sc">-</span>eps_tb,</span>
<span id="cb10-113"><a href="#cb10-113" tabindex="-1"></a>      <span class="at">EB_TB_high =</span>  eps_tb,</span>
<span id="cb10-114"><a href="#cb10-114" tabindex="-1"></a>      <span class="at">EB_CS_low  =</span> <span class="sc">-</span>eps_cs,</span>
<span id="cb10-115"><a href="#cb10-115" tabindex="-1"></a>      <span class="at">EB_CS_high =</span>  eps_cs</span>
<span id="cb10-116"><a href="#cb10-116" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span></span>
<span id="cb10-117"><a href="#cb10-117" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb10-118"><a href="#cb10-118" tabindex="-1"></a>      <span class="at">label =</span> <span class="fu">paste0</span>(</span>
<span id="cb10-119"><a href="#cb10-119" tabindex="-1"></a>        <span class="st">&quot;TB[&quot;</span>, <span class="fu">fmt</span>(EB_TB_low, <span class="at">d =</span> label_digits), <span class="st">&quot;, &quot;</span>, <span class="fu">fmt</span>(EB_TB_high, <span class="at">d =</span> label_digits), <span class="st">&quot;]</span><span class="sc">\n</span><span class="st">&quot;</span>,</span>
<span id="cb10-120"><a href="#cb10-120" tabindex="-1"></a>        <span class="st">&quot;CS[&quot;</span>, <span class="fu">fmt</span>(EB_CS_low, <span class="at">d =</span> label_digits), <span class="st">&quot;, &quot;</span>, <span class="fu">fmt</span>(EB_CS_high, <span class="at">d =</span> label_digits), <span class="st">&quot;]&quot;</span></span>
<span id="cb10-121"><a href="#cb10-121" tabindex="-1"></a>      ),</span>
<span id="cb10-122"><a href="#cb10-122" tabindex="-1"></a>      <span class="at">label_col =</span> <span class="fu">ifelse</span>(category <span class="sc">==</span> <span class="st">&quot;Both&quot;</span>, <span class="st">&quot;white&quot;</span>, <span class="st">&quot;black&quot;</span>)</span>
<span id="cb10-123"><a href="#cb10-123" tabindex="-1"></a>    )</span>
<span id="cb10-124"><a href="#cb10-124" tabindex="-1"></a></span>
<span id="cb10-125"><a href="#cb10-125" tabindex="-1"></a>  out</span>
<span id="cb10-126"><a href="#cb10-126" tabindex="-1"></a>}</span>
<span id="cb10-127"><a href="#cb10-127" tabindex="-1"></a></span>
<span id="cb10-128"><a href="#cb10-128" tabindex="-1"></a>plot_overlap_blue_EB <span class="ot">&lt;-</span> <span class="cf">function</span>(tab, ci90_vec, <span class="at">title =</span> <span class="st">&quot;Overlap of equivalence&quot;</span>,</span>
<span id="cb10-129"><a href="#cb10-129" tabindex="-1"></a>                                 <span class="at">cs_mode =</span> <span class="fu">c</span>(<span class="st">&quot;pooled&quot;</span>,<span class="st">&quot;overall&quot;</span>),</span>
<span id="cb10-130"><a href="#cb10-130" tabindex="-1"></a>                                 <span class="at">label_size =</span> <span class="fl">3.2</span>) {</span>
<span id="cb10-131"><a href="#cb10-131" tabindex="-1"></a></span>
<span id="cb10-132"><a href="#cb10-132" tabindex="-1"></a>  cs_mode <span class="ot">&lt;-</span> <span class="fu">match.arg</span>(cs_mode)</span>
<span id="cb10-133"><a href="#cb10-133" tabindex="-1"></a>  cs_label <span class="ot">&lt;-</span> <span class="cf">if</span> (cs_mode <span class="sc">==</span> <span class="st">&quot;pooled&quot;</span>) <span class="st">&quot;pooled SD&quot;</span> <span class="cf">else</span> <span class="st">&quot;overall SD&quot;</span></span>
<span id="cb10-134"><a href="#cb10-134" tabindex="-1"></a></span>
<span id="cb10-135"><a href="#cb10-135" tabindex="-1"></a>  sub <span class="ot">&lt;-</span> <span class="fu">paste0</span>(</span>
<span id="cb10-136"><a href="#cb10-136" tabindex="-1"></a>    <span class="st">&quot;Notation: EB_TB = ±ε_TB (ε_TB = k × SD of bin means);  &quot;</span>,</span>
<span id="cb10-137"><a href="#cb10-137" tabindex="-1"></a>    <span class="st">&quot;EB_CS = ±ε_CS (ε_CS = k × &quot;</span>, cs_label, <span class="st">&quot;).  &quot;</span>,</span>
<span id="cb10-138"><a href="#cb10-138" tabindex="-1"></a>    <span class="st">&quot;90% CI (treated − control) = [&quot;</span>, <span class="fu">fmt</span>(ci90_vec[<span class="dv">1</span>]), <span class="st">&quot;, &quot;</span>, <span class="fu">fmt</span>(ci90_vec[<span class="dv">2</span>]), <span class="st">&quot;]&quot;</span></span>
<span id="cb10-139"><a href="#cb10-139" tabindex="-1"></a>  )</span>
<span id="cb10-140"><a href="#cb10-140" tabindex="-1"></a></span>
<span id="cb10-141"><a href="#cb10-141" tabindex="-1"></a>  cols <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Neither&quot;</span> <span class="ot">=</span> <span class="st">&quot;#F7FBFF&quot;</span>, <span class="st">&quot;CS only&quot;</span> <span class="ot">=</span> <span class="st">&quot;#CFE8F3&quot;</span>,</span>
<span id="cb10-142"><a href="#cb10-142" tabindex="-1"></a>            <span class="st">&quot;TB only&quot;</span> <span class="ot">=</span> <span class="st">&quot;#7DB4E6&quot;</span>, <span class="st">&quot;Both&quot;</span> <span class="ot">=</span> <span class="st">&quot;#084E8A&quot;</span>)</span>
<span id="cb10-143"><a href="#cb10-143" tabindex="-1"></a></span>
<span id="cb10-144"><a href="#cb10-144" tabindex="-1"></a>  tab <span class="ot">&lt;-</span> tab <span class="sc">%&gt;%</span></span>
<span id="cb10-145"><a href="#cb10-145" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(<span class="at">category =</span> <span class="fu">factor</span>(category, <span class="at">levels =</span> <span class="fu">c</span>(<span class="st">&quot;Neither&quot;</span>,<span class="st">&quot;CS only&quot;</span>,<span class="st">&quot;TB only&quot;</span>,<span class="st">&quot;Both&quot;</span>)))</span>
<span id="cb10-146"><a href="#cb10-146" tabindex="-1"></a></span>
<span id="cb10-147"><a href="#cb10-147" tabindex="-1"></a>  <span class="fu">ggplot</span>(tab, <span class="fu">aes</span>(<span class="at">x =</span> bin_f, <span class="at">y =</span> k_f, <span class="at">fill =</span> category)) <span class="sc">+</span></span>
<span id="cb10-148"><a href="#cb10-148" tabindex="-1"></a>    <span class="fu">geom_tile</span>(<span class="at">color =</span> <span class="st">&quot;white&quot;</span>) <span class="sc">+</span></span>
<span id="cb10-149"><a href="#cb10-149" tabindex="-1"></a>    <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label =</span> label, <span class="at">color =</span> label_col),</span>
<span id="cb10-150"><a href="#cb10-150" tabindex="-1"></a>              <span class="at">size =</span> label_size, <span class="at">lineheight =</span> <span class="fl">0.95</span>) <span class="sc">+</span></span>
<span id="cb10-151"><a href="#cb10-151" tabindex="-1"></a>    <span class="fu">scale_color_identity</span>(<span class="at">guide =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb10-152"><a href="#cb10-152" tabindex="-1"></a>    <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> cols, <span class="at">name =</span> <span class="st">&quot;Equivalence overlap&quot;</span>) <span class="sc">+</span></span>
<span id="cb10-153"><a href="#cb10-153" tabindex="-1"></a>    <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">&quot;Bin width (minutes)&quot;</span>, <span class="at">y =</span> <span class="st">&quot;k multiplier&quot;</span>, <span class="at">title =</span> title, <span class="at">subtitle =</span> sub) <span class="sc">+</span></span>
<span id="cb10-154"><a href="#cb10-154" tabindex="-1"></a>    <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">14</span>) <span class="sc">+</span></span>
<span id="cb10-155"><a href="#cb10-155" tabindex="-1"></a>    <span class="fu">theme</span>(</span>
<span id="cb10-156"><a href="#cb10-156" tabindex="-1"></a>      <span class="at">plot.title    =</span> <span class="fu">element_text</span>(<span class="at">face =</span> <span class="st">&quot;bold&quot;</span>, <span class="at">size =</span> <span class="dv">18</span>),</span>
<span id="cb10-157"><a href="#cb10-157" tabindex="-1"></a>      <span class="at">plot.subtitle =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>),</span>
<span id="cb10-158"><a href="#cb10-158" tabindex="-1"></a>      <span class="at">axis.title    =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">14</span>),</span>
<span id="cb10-159"><a href="#cb10-159" tabindex="-1"></a>      <span class="at">axis.text     =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>),</span>
<span id="cb10-160"><a href="#cb10-160" tabindex="-1"></a>      <span class="at">axis.text.x   =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">hjust =</span> <span class="dv">1</span>),</span>
<span id="cb10-161"><a href="#cb10-161" tabindex="-1"></a>      <span class="at">legend.title  =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">12</span>),</span>
<span id="cb10-162"><a href="#cb10-162" tabindex="-1"></a>      <span class="at">legend.text   =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">11</span>)</span>
<span id="cb10-163"><a href="#cb10-163" tabindex="-1"></a>    )</span>
<span id="cb10-164"><a href="#cb10-164" tabindex="-1"></a>}</span>
<span id="cb10-165"><a href="#cb10-165" tabindex="-1"></a></span>
<span id="cb10-166"><a href="#cb10-166" tabindex="-1"></a><span class="cf">if</span> (<span class="fu">exists</span>(<span class="st">&quot;df_rdd48&quot;</span>)) {</span>
<span id="cb10-167"><a href="#cb10-167" tabindex="-1"></a></span>
<span id="cb10-168"><a href="#cb10-168" tabindex="-1"></a>  cs_mode_use <span class="ot">&lt;-</span> <span class="st">&quot;pooled&quot;</span></span>
<span id="cb10-169"><a href="#cb10-169" tabindex="-1"></a>  bin_set_use <span class="ot">&lt;-</span> <span class="fu">seq</span>(<span class="dv">10</span>, <span class="dv">120</span>, <span class="at">by =</span> <span class="dv">10</span>)    </span>
<span id="cb10-170"><a href="#cb10-170" tabindex="-1"></a>  k_set_use   <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span>, <span class="fl">1.0</span>)</span>
<span id="cb10-171"><a href="#cb10-171" tabindex="-1"></a></span>
<span id="cb10-172"><a href="#cb10-172" tabindex="-1"></a>  tab_cabap <span class="ot">&lt;-</span> <span class="fu">build_overlap_singlecs</span>(</span>
<span id="cb10-173"><a href="#cb10-173" tabindex="-1"></a>    <span class="at">df        =</span> df_rdd48,</span>
<span id="cb10-174"><a href="#cb10-174" tabindex="-1"></a>    <span class="at">outcome   =</span> <span class="st">&quot;cabap&quot;</span>,</span>
<span id="cb10-175"><a href="#cb10-175" tabindex="-1"></a>    <span class="at">time_col  =</span> <span class="st">&quot;running_var&quot;</span>,</span>
<span id="cb10-176"><a href="#cb10-176" tabindex="-1"></a>    <span class="at">bin_set   =</span> bin_set_use,</span>
<span id="cb10-177"><a href="#cb10-177" tabindex="-1"></a>    <span class="at">k_set     =</span> k_set_use,</span>
<span id="cb10-178"><a href="#cb10-178" tabindex="-1"></a>    <span class="at">cs_mode   =</span> cs_mode_use,</span>
<span id="cb10-179"><a href="#cb10-179" tabindex="-1"></a>    <span class="at">unit_time =</span> <span class="st">&quot;mins&quot;</span></span>
<span id="cb10-180"><a href="#cb10-180" tabindex="-1"></a>  )</span>
<span id="cb10-181"><a href="#cb10-181" tabindex="-1"></a></span>
<span id="cb10-182"><a href="#cb10-182" tabindex="-1"></a>  p_cabap <span class="ot">&lt;-</span> <span class="fu">plot_overlap_blue_EB</span>(</span>
<span id="cb10-183"><a href="#cb10-183" tabindex="-1"></a>    tab_cabap,</span>
<span id="cb10-184"><a href="#cb10-184" tabindex="-1"></a>    <span class="fu">common_ci90</span>(df_rdd48, <span class="st">&quot;cabap&quot;</span>),</span>
<span id="cb10-185"><a href="#cb10-185" tabindex="-1"></a>    <span class="at">title   =</span> <span class="st">&quot;Cabinet approval: overlap of equivalence (1st gold medal)&quot;</span>,</span>
<span id="cb10-186"><a href="#cb10-186" tabindex="-1"></a>    <span class="at">cs_mode =</span> cs_mode_use</span>
<span id="cb10-187"><a href="#cb10-187" tabindex="-1"></a>  )</span>
<span id="cb10-188"><a href="#cb10-188" tabindex="-1"></a></span>
<span id="cb10-189"><a href="#cb10-189" tabindex="-1"></a>  <span class="fu">print</span>(p_cabap)</span>
<span id="cb10-190"><a href="#cb10-190" tabindex="-1"></a></span>
<span id="cb10-191"><a href="#cb10-191" tabindex="-1"></a>  tab_bounds <span class="ot">&lt;-</span> tab_cabap <span class="sc">%&gt;%</span></span>
<span id="cb10-192"><a href="#cb10-192" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">select</span>(bin_minutes, k, category,</span>
<span id="cb10-193"><a href="#cb10-193" tabindex="-1"></a>                  EB_TB_low, EB_TB_high, EB_CS_low, EB_CS_high) <span class="sc">%&gt;%</span></span>
<span id="cb10-194"><a href="#cb10-194" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">arrange</span>(k, bin_minutes)</span>
<span id="cb10-195"><a href="#cb10-195" tabindex="-1"></a></span>
<span id="cb10-196"><a href="#cb10-196" tabindex="-1"></a>  knitr<span class="sc">::</span><span class="fu">kable</span>(</span>
<span id="cb10-197"><a href="#cb10-197" tabindex="-1"></a>    tab_bounds,</span>
<span id="cb10-198"><a href="#cb10-198" tabindex="-1"></a>    <span class="at">digits =</span> <span class="dv">3</span>,</span>
<span id="cb10-199"><a href="#cb10-199" tabindex="-1"></a>    <span class="at">caption =</span> <span class="st">&quot;Equivalence bounds by bin width and k (cabinet approval, 1st gold medal)&quot;</span></span>
<span id="cb10-200"><a href="#cb10-200" tabindex="-1"></a>  )</span>
<span id="cb10-201"><a href="#cb10-201" tabindex="-1"></a>}</span></code></pre></div>
<p><img role="img" 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s6mNuHP5xAqvs72FDXSBJX69ZfukffKZw/QRlPx0Z/Ism8tJl1vLVz3mMvOIYv7iHMZeKoGRydmF5pNK5HXPMB5TuGz7Tcy6o1N6gyj8/vSkcD3kfom+skDnles+Or+7Zj1eEz3/Tuq90FYrx70H+fiCLlQE+NpmRnK1Q9ogmRVmyHnY+LWZkqbDRfz6SpRnqbiN0VVVJn/85Wzqt/sGHLkjekB2gzvCrpl0E6Sk3xriRF+Ope70rqIZnL3aEznkPRvkI0JPBODoxOzCwvNZrNZn5+froYk89lc0kqp3+hvb9yfJ5PmRZQvHQl8H/k853Jun+apu8bMPtXZaPfpa+X8avqIzwjZw2mskP4qjw+W7FANfIqX1fZdZrUZEn3s2kxps6GUM5vQu12UL0P1TU4UT1ue+0pNNUH1fT5TdvWbGUOOvCk9QJvxXUGXLNpJYpJvLTFSLyO+/h0Gd4/mdA6RU5/GTGVgoFKpjI+Pz8zUarWG68eY/Qs836cOBkdHJ2Zbybzf7+QzSTlT6jf62xvHCEp5as9MUb50JPB9pX554eJkaTy8j2zM7FL+Fqzr9HV2iXNgx96niZY2Q875ZXgaJKg6e6jNlDYbSvmK0pARSVmegFuwFc9qem6f9yRbwPOV832aKx4zjrwpPUCb+V1BlyzaSWKSby2qLQYdeJ/T917Kh+34lzW90wIGd4/GdA6RE6/684sii4uLc3Nzk5NjY2OlkusRbHkKPNv9NhqNRq1WmxkfH69UKpWBAb85+eDg6OyC6nkL/bsEH3kq7tXf3jiloAwR4UtHAt9ffm+qmBsLfJBlozZe8XmUi84rkvx6Qu05gZr/rQPKDWcy0dKlek6u/68ZFKet+zeYp1Sb/tXZW22mtNlQyhEp6IdqMcrjbUWL3pccRX2JWVr1mxkzjrzBPUCbYV1BlyzaSVKSby2qLQYMqao3r0Xgl1yk2qmY2z2a0zmov0S+P8tUJj6OcyJ9C1znZeXpqY0PlEql0tjY2OTc3Nzi4uJijDsPsrlAFbHf0P5a9LU3TisoM0T50kV9HgWSFvSs1HK52noyi/3hkAdiep91oD5BoHgmgt+NQOXpuuez/u/HrM57P62OUHe7iqJqfiza5/VLqQ5f+5PKD2pKpTb9W6D+pxWfzHCzeofUp0CqJtusax/pLmGvjg0uair1G+n7Ev/wBjDgyPuWspceIP7BDK8yk7oCXam1E+WGoz+NK7ysSbeWqNWkbAARxl+f7aoOVdRR2P9QhJQqbO1MuscUpgepTGP8D1LMkqYyL9Kg2G/8L2zkjjjCQfTbvvaXptfh1bfX9H4yaOKuv93eemO9ZqwfVEBvGLfBRGouaXxY+0tHAp8HYaOaLmV79dl42fE9DD4roD1GOLZcD92ydlmzSuADjtS061Cp41F/ZZVfxPahr9fnp/2GhAihSI+1Gfi6HWf00Wozw83qDoF+7dbZZKO2Wd196JU0jfrtfwJvwpEP2ELsHiDNBN6krkBfSu0khQQ+jfFCY4thr7TS/f5bZehsNviF14kl8AZ3j4lXd0rTmMCjFKekKcyLdHh3G39b0Ttiv55Q/5KasmoSnxA2m029fiPkjfZRGlJvvbFuMw7aefi+AwoQru8JvPaXjgQ+HxJ4ZWnZr7X2enogagIfc5t+280uge8hLt8vbOgWo/YxKdRmz40vwsmLdDarP7L3cPh0aypoF+EFTaF+85DAG3DkeyulcvupJvAGdQVRpNJOUkng+zJehEl2XAvaarwE3uTuMeHqTm0a08PXWL+kPWxOk3evsb+xcTri3pqa39YTnxD6xhdNhBl5b1uM0IzTSjNiHc6ME3jN6PkNfD4MTiyEnCELVq7OL/i9bVPx9NdItH77U9Z9P6aISHU+p28GHZ2N1RNW5xf83nsT+jDVqE9myaQ2I9VnQPRZbVZfzBqW6rz222zVT6a0tuL7fqTO2tnUb/Zyf+StTSTeA6TH7K7ATwbtJDHJtxa/h+L0JlpLiTSWR2Zw92hM5zA4sRBrNulX0v4E7p07ZfoYu7gdkUhQ5IlPCEXErnDdD5er+m2jr71xtM4w3W4re1qHngQ+NwZHZ+Ml8eXqfN03excRGZw4ovlFKE/XFT9m0elSrt2rOWSUq/P1nKbvItaZlEg9Vkg8Pq8K7EEWtSkidx8J+MFUR9TaTGmzEUQ/V1aerkcajXwnmVrPZ8qofvsg70feknQPkCKzuwJf6beT5CTfWjSnzOXqfIQrVJFaypG7dTcbi8ndozmdg8+T0f0Fl7QfgSueeZjJc907BYh1WS3sQCY9IbTp9xsLd8qi/nb72huPzvo/l8O9z+r8wt5+PvYwDTpfuni3GCBF9fmq7rkk129Rwrcatinrg94m47nHw+/mpnrw7/PK1Rh3LWV5C33naAUH0o5H6wdLwV/CeLdMJVmbAbf1BB2HsNrMfrORD2FoHWvWsE4ZI9V0ovWbj1vo3cHl9sh3NpRAD5DuLfT2PgzoCuJJsJ2kdAu9Y/sJjhfNsEptbSfiuBY6D7e3q/n4sJi30Kt3oL2uViTpdY+tAJKo7vSnMYk3zKTbeejuuvcW8zvb06gW9tgJZ+BaP2FPYULYLmpwvzEdp9/obDqR3jjG1y346+4fltG30DvWCQg/XoBIX70+P12tlrtvDCmXy+VqdXo+3vS2Pj/t/hqWPQ+6V7ax0OcnOT5g78SxEXsn4eXLTQJvRzI/Pe2pgbLikGltyn1MxK7HiNtxbTOh2gzpVDy70azN/mw2qlYdd1VxdTrW02QCChm9jEnVb94S+Nae8nvkPWWM2QNkkcBbO8p3V9CTJNpJ2gm8u6QJjBfNpu/R7mwoVkYX3lJST+AN7h5dBeixurOaxqi/Qj1MQBJu50F76q6+mH15EqNafV45IbcDj9oNpjEhbG3a22+4u+kevrkJ9MYx9+6XVQSFVYwE3l5T+aUbaDab3sMJYNVozFRK3S8VLk/Xe/3hXkqbBZASvrMmqY0PjM11/a0fjwIAUuLpkOiNeqfoNzisZuI38AAAAAByw/M7+MUvP5HXZ7wAWSOBBwAAAJAfZPBhGjOVgUqlUhkfHx+fqdVqtUajEXiEGi8+5/nbtVdw+d1EJPAAAAAAcsTz2HYyeK/FxcXFubm5ucmxsbGxUqlUGhiozPgcpdqB7h9JRXpLC/KEBB4AAABArnS/d5AM3mXwCuXr0xYnd43XXJfiG43azHjF+9gM8ndzkcADAAAAyJfui/Bk8C6jd6rfLL84N1YqDXSUSmOTc6o3wJO/G4sEHgAAAEDedF2EX5w8UOtbWfJndFb1ajZd1XleW2EsEngAAAAAuTM4ccSVws99hQzeYXRW9YLxcOXqfJ303WAk8AAAAAByqOs++rl9fg9pW50GJxaa9emqfhZfLlfn6wuzozx93mQk8AAAAAByyX2nOLfRewxOzC40m/X6/PR0tVoul73ZfLlcLlenp+frzeYCyXsBDDSbzX6XAQAAAAAAhOAKPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAS+bxozlQHbeC3007Vx66OVmUZCu6/N1BLalHpr7fg0ossNR6XocQcXunqlUhmfqTWSqsOopXU3oYxLWzCmHHx7R5l8C005JklIvEdOT6NRmxmvVCruQzk+HjQChFdlpVIZn0lyENFlReOMJLBtxxmIjBy8iiXLfsu9S319Hf39gvB+2aPsN05fAZVcV0Qfvl2eIuT5+CQhkxlCE31Sny53qqE6H/Lp+ar1wfJ0vfc9z1fLCW0qYGvt+EJjyxFXpehwB6e9erk6n8DBj1xaa9+tmsq4tAVjxsG3d5PRd9CMY5KMJHvk9Ni9c+DRVx7LCFVZrmZ4CFTlCm7dcQYiIwevosm272qaNvp7iz8/HfJlD95x7L4CbrmviMy/WV27z/vxSUYWMwSuwOfD3Fh258JqB8bmFnO6tdVicW6sZM7FHbNKWzC9HPzGzK7JRZHq/OxosoXqMxqkntp4KbR3Xpzs9Vguzk2WQq6CJ6Z2YNITT/maUib7RrYGJ45Ml0Xmxgy4ySWSNLqvxkylNDYZ8mVfnBsr+R3MbPqK4st/RfR3VpD/42OU1E4NIITnfG3g+bAEz+Yke2LIjAtRunq88hK2et117jGtQ9aqkrAg8lHagsnRwbd30f+Ky9ExSU7+O772YRcpV6fn685y1t3HMuAGKp/4ujeQzYHoDJr6e+NqusHsNpzNl8zY0d89l7S+7I6t1+vz1XLw/Z499RVoMaAi+jorMOD4JCeLGQIJfN8obrgKGDdI4DOR8hBuafdBKR2zpPKlrq3lrIZb5crZtDw3B7+1gxwcn9wckyTlvePTynUdg1DXMQ9L4BVbyOBQcDv8apNlN2bo6O/IaQK/647P+X3Z4/UVfZabmYABFdHXWYEBxydJ3EK/OpSrrZNGWd5Ij74ZvdP+Zi8+X+9vSXSYVdqCiX/w7XuNy9NTxbp7ngapqf68dZthefrIxKDfh+y7lEVE5r4Sa+wZnFhoz7cWJw8wfiFRgxN7qyIic/sKch990t1XY2bfnP2f5en6QsB3fXS2ndp0Hc2M+opiM6Ei+jkrMOH4GIcEPgeumeo0uJgpfMP7ZF7F0xytxyKO2V+jxcmS6lmqQY+HjLK14Af56hVYsR3lespj0noIZH6fE23Wjze1S9s+8FqyqJ1GzdlmBiqV8Zw8ylxbxKbSGizLd3/Qb5xbRcck6wbpesGI9sZq4wE/JG/MVNL5ogx+8O44Dx50Gp1tp/DRJ0yaA4FdhaXWL+DnxuLWlrPd+44eysEr9kjkp+sxxQ3XV7J7c43azLj70fsBO7M2pTO8OtbQGfR7PQ7dBdMo2ejUdFnUZ4fyP8QHSGb0bzzxZfsrEZjUWFpnQ0QWn69HP2KR+4rVNBPIdUVY+joryPXxMXeGkNq1fYRw36MYdttHwO0YAc9k7HqYo/MWFgfnDkOepOrcfcjW/G8o0y9w13b8C6c6ZnHuVcv0Jrq8/wbevTX90vo0DL8qj3kUtG+c8ytOOg/P7vfBd23cd7XVdUxSaZB+xXD15NoHs90lqtZxdoG6G9S+Bz6R1TtHOErHGWEg8K3C8OI5W5V6BPFuQ9kSY49EfpxNKLBkfgdKubOAIdzn4c4RBv1ejkPgboLq0bc7S/h2dANH/84x1Sxz3fWj4+6tJDwlWUUzgXxXhGvjfZkV5Pv4GDtD4Ap8Tjhv+4h0FT7wmYwRH3famKmUvA/5dW1wclevZ59iF3huzL9whjyp1rrMYd20oHEess/MKq1aY6bSukek2+Jc7405Pb0c/Najun3OtK/KY5INRw9ana/PjmqXsHSN/R+ex+/WxtubLIvoh9y+CrE4WUr97or2fcHy3Iu6O0py5NLzlXH1CLI46fvIY6XkR6InfEu2a6bRmNnlc6C8U4XgIVx1VHsY9CMdh5pPiO2d+Nf34BXXWp/58hO57Zs0pNB9dS5rVu/Uuyl6cHDQu99M+4o+SXfUM6Ai+jorMOD4ZCPpGYJu/o+keU8mBV2F9zmb0/1Mxta2XQ9j7NqY7yngzsbK1XnPwyHby9Ql0HwPfPQCd523dxbNdWEikXNy0d4E6x9z2Iqpvsc68gXPvpY2Lq3z7s6HnnQaW32+Wi6nFFUODn7ImfZVeUyS5+344p1Zb1M8Dq7u6i4jXxb0HM9yuVqdnp9XXNrwX1evX418gSXOyNXjQ+w8jcc1fqgHnYC703ofidzXfbq259/m6/PTPsX2OajuAdxvFe1BP85xcHQ6XbtxBON/4LJ5YqR5o39ytyD00lf0WR5mAvmviP7OCvJ/fJKXxQyBBL5vVDMk/4csKkew4Gcy+i31GwyDp9l+M7RICXycArX+kmcAACAASURBVNf9ZivB5YonoyE83RQk6Xyp/wlTtGpxxJ39o8L7f/DDkpzVeExS0HUYHSlX/MK5E/Ykzk4G3aXueQ2Pa8VUE/h4I1fvCXzAzfLqQccvgU9kJHLWtqdgwY9sVk6IQ2bJqsVxBv04xyFwP+G1ms2bBMwb/RP9DUHsviJDeZ0J5L4i+jwryP3xSUEWMwRuoc+Vzo30OveqO25L2au4H8vxIAitW89GZ+1GMqu6x6V1G1sveiyw8tmZnedV6N+82X+Lc2Ml9dP98sis0qoY/MDyyAe//aTWkIckraZjkrbaeMm+/zDafXFdBkdn2791W5xr3TYd/Mze0C0uND2vbLd2sDg3OVYqeZ6WloGERy5dqvuWY+0r8ZFIcRg6v0pQFVv1LPPaV+Z8Py/ieNigI9LeBn3941C6pvUDwX3e1jY4sRBQCFdBTBrkfaXQfV17Re835Oezr0hY2qNeXisiL7OCvB6ftKU0QyCBz5koKXzrO+n7sxL7+a29fCsbjUatNjM+Xhnw+4FMBL0VOODZmcnTOtPvP632Wb3u6nVy88N9s0obRXueOzdWyVmP3pL0wW+8+Jz1H35D5So8Jul6YrzdOVbnp2KPzbbR2SPOq0zl6nzs7L1lcHB0YnbBOoLz89PVatk58Vmcm+z+3X3ashi5vHwGkOjv9Up8JAr+XajmnLedv/uXrpVGh0SqO+hHOA6dUwGLk2Ml6/nW4zP63Y9myRNi3uif2ImN3PUVCclq1MtpReRmVpDT45Ou9GYIJPC5o53Ct7+TGu8jifC1cb9PplQqjY1Nzvk+bCiKlApskEGr12nfzpTvFyfHLW3y7+RoX6JxCPjlm+NCzuhs+3ylNXEc8HmvYd6k2FRW3TFJ9SUxi5OTnSxnbqznWqqN73I+7Gsx0Yn+4ODo6MTs7II18XGkEwk8ndSi83KsPg0EfnPX9tXhYmi/0NWr/Ri57qOa3qDf4njXoFXKxbm5Sbv7CX2TXDGk0KWn2XQT6CtWz0wg5xWhYVUfH2NnCCTwORTtRvqgM/RR73tvzFQGSqWxybnFxSQH727JFdhMnds2Y7w4OXNmldZjdPaI/0+mci+dg88xSVq12r6A0cOsp1EbH2g9cLzzG/ioD0nXZV3CqLfHml5mFu1r6pHkbCAw+qxx+6xI5BWzGfRldFZ506vYl8tKevNio+tIRJLtvtpfkwi//2jEOVeSZF/RL2mOekWoCI5PmtKZIZDA51K0FN5/TIs2qHte9FIul8vVanV6en6+HvFViYGSKrC5zLroY1Zp3WrjlYC3VRkghYPPMUlWdb4+O9u+whh3gG7MVFo/k5PydH1hdnah/cga6605+ttqX1EIX6mTT/SQTrRu3454d3nOBoIkfp/ZfzqPierc/p3ZoC+tyXWzPj+tTOS1TlQVoY4S7L7am9L+bUHtQMm+7cH+tmfdV/RLuqOe+RXB8UlPajMEEvic0kjhOye1fL8S7esiGsNeY2Zfq220HpK4sLCwMDs7OzEx2uvPNtIoMHKq/WAkLT3/wjdAY6ZiD0nl6nT72aS+j0paFVbhMUm1QZan7WfSjPYyQDvfxt15aJ37kTVjlXHdHyd2nhmmMX/pPZ9o5+96/XafBgK/0wXx7h7IG42D6pX+oK8wODrh/PWqM5k3/E3v2es8MXBun9ZdOp2vauvnKyn2FatoJpDvigi1yo+PsTMEEvjccqfwLyo+EdqgO9dFwn9r2H4wcHn6iOohiZ3vU3yJFtho7TmjEYGaVdoOV5ueGDTzlFDUgx/ywOZVeUwy0n70WvQBuv68/R/V+a5Hzo92fjWrn2c6pkuhk51Oi4h5MGvjncsCiqeSK/RnIPDJbNv7Cn6UXO5Fmt9ashj0rR3NVFQXzwYHR+1kvn2ric/ZB92HaBshye6r803XuVmz81XtvPcg076iTzIY9fJdEf2fFeT7+GQk8RkCCXyOOVP4ScVIGnJSy3F2vden5jY636deZFfgfOsEasKdBmaVVldjppKbh5QGiHHw4z+wubjHJCOdHlv3OkPL6OzCfLVcnW+q8qjBiYX6dLl9Il+rJJ2uNvjCfWOm/UScWAezMVNpDw3Kd8KFlC7LgUA1aXKcfcjVVC86Z5Wra7ydRmt9yxMa9K2ytfKHiF+LdlnCHqJtkIS7L0efE/wbhEZtvKI805ZZX5FPSY16+a6I/s8K8n18MpL4DCHKnQNIUvthpIG/Wev+EVrXhx2Ly9Xp+dYy99Ni/NZx/72zqfbddN5NBazWHUY7PtfDQaMXOOw46R1HTepCJ7h6YM0kpH2MQ4LIR2nTpGzT9XrnUS2upp7sLvt38FubVq+yOo9J8lpB+fZ7/f6+dArS7m3rzn61q2fvKm1ot+t5D2+0cOOMXPH6Z89xaB0F975cW1TuJ/GRKPh7EWOpc7ZQrs47qtv5BXeUL9agH+s4uOp7vqshdnbmU69+X7ZkGTv6dzVx67veWVqfn3a9ZMu74976CgNkNOrluCJyMSvI8fFJXhYzBBL4vtEd7t0pvOfDoY+Z8Ywm7u9A5xO6D6xxF8Fva77DWdQCx542+Z5aCOCJJpxyihd91QRFzpf6WlodsUsa1tiSjykPBz94urs6j0ni/A9yflJ4/WeQhY8SwWJEGn3k6jGBbz8HOHxfhibwWoO4q3RxBv2Yx0FjV3616vddizPEBzB59Nd+fnhZvd9e+ooM5X8mkNuKyMmsILfHJ3FZzBC4hT73ul+g6llc9/9KlKvzdc+DKDo3otjsn8V0XgSp2tD0fPv+F9dNOH5bS7DAhWTdDNPvUugyq7Qdo7P+I355um5GTJEPfuuXVupHQq3OY5KhqK8BTa8go7PNuvrNXQ7lao+VXvb8bl9L9gPBneqWX6RBx/HUQ6XO449an48z6McsWmD2Va5O+9VC66f4xfllXfLd1+jsQsDXqbPb+oJ6vxn1FX2T2aiX24rIyawgt8cnQ8nNEEjgDRCSwg+Ozi5033xi3ZzSXFD+aHJwYsH9DWgNzqOzC56bWFobmhj1eTOD79aSK3CBlFu3Dfl0T/liVmmVBicW6vX5alnR1lJ86G0Sejj4rbNqPg91XpXHJEudt9j0OYUXGZyYXWjdm+js2K1DafftcSq9XC5X23URr9VkPxAMTrhmj9bOCjbojM4uNO367vyx/b3x1HWsQT+ewYkF722yraL5N8NW/q79iIXcSrf7GhxtfdWriq+6xjc1tb4iF7Ib9XJaEbmZFeT0+GQpqRnCQLPZTKhIAIBcqI0PjM15r7gBgEFCerLGTKU0KXRzQChmBQXDFXgAKBr7hrnFyQO9XzkDgH5oPbPd9/J7/fnF3D1rGsglZgUFQwIPAIXT+p1VzBc3AUCf1Q5MLoq43ybl0njxOTH+DYBANpgVFAsJPNBvtfGBWLxvNUZkxT349u+sYpxuL+4xAWAK+/J7efqI+vJ7ozZemlw0/MfxdLY5sToqgllBkZDAA0Ah2Y+WnRtjDAVglMbMrsnFgPRdZLBYrxAAMsCsoDhI4AGgoOxb5hisARjETt+r80GP2xqdLdgrBID0MSsoCp5CDwAAAACAAbgCDwAAAACAAUjgAQAAAAAwAAk8AAAAAAAGIIEHAAAAAMAAJPAAAAAAABiABB4AAAAAAAOQwAMAAAAAYAASeAAAAAAADEACDwAAAACAAUjgAQAAAAAwAAk8AAAAAAAGIIEHAAAAAMAAJPAAAAAAABiABB4AAAAAAAOQwAMAAAAAYAASeAAAAAAADHBRvwuwGg0PD/e7CAAAAEjR8vJyv4sAoIC4Ag8AAAAAgAG4At83fTkv+6Mf/cj6j0svvTT7vSeuYOFI4SIinJwrWESEk3MFi6hg4UjhIupvONxrCSA9XIEHAAAAAMAAJPAAAAAAABiABB4AAAAAAAOQwAMAAAAAYAASeAAAAAAADEACDwAAAACAAUjgAQAAAAAwAAk8AAAAAAAGIIEHAAAAAMAAJPAAAAAAABiABB4AAAAAAAOQwAMAAAAAYAASeAAAAAAADEACDwAAAACAAQaazWa/y7DqDA8Pi8jy8nK/CwIAAICEMdMDkJ6L+l0AdHznhz/rdxESc/3b3lKkcKRwERUsHClcRISTcwWLiHByrmARFSwcEbn+bW/pdxEArCLcQg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAAJmgic1u2bNmyZYv376cvrNj/lo5P7Rxp19HQzv2HllY6S4P/LT1809qEGsfwyPaHjp/o2v6x3Y5PjEwtqctcrk4f8lmkCCfgA0vHp3ZelVA8USLyFGm+rl1BIUsLVkHa4VySYjiuCuq1vQ3pxaOB9tZCe/MJh/aWRDgptjcqKIlw0q2gzqJL1l7e3rUn8P3Oitx+zL2LqP9UFaQ50wOA3pHA90FYAn98u2KodAyEQf+U6/ZmeL9rhFYPzwFlDg0n6ro9C49Iud+yRrDxlvamYBUUP5xyHsMR2lvX0pxVEO3NRnvTXLdnVJBraaoVNNJJ1JNM4NUVNF3XmukBQO9I4PsgMIF/dmrYHhTtk8dLx9t/6T7V3f3Pf10Rkd2HgoaikY3do+bKiWOtAW/n8e7h2VUYxX6ny9ZfPvGRkHAC4g1Y995b/cr8UPuk+MiDnmD1IgqugnSWim8VPzjsuzS/FeSMyJ42jWw/9qzV3irtpc5i9xaORpuJEs6jVkmuHCpZf7Ham4iI3HmvO5yVQ3eqw8lRe7vuDuubsv3gw90VtPN4a8JtR3Rop3Oxq9PIUXs7aJW5VUEiIrfssEOzu4WCtLettzhrx9XePF16XtpbacQus7e9ye5DBWhvzgrqlPnZgFjyVUGdpQ/v6FyNv+WQszxmV9D+E53BSF1BPSTw/kVyp/Ak8ADSQwLfB0EJvJ3tdI399unekCEncN2wBF5EZOihZ7uX2mO5Y13v8Kzab7M5b232kuBwAsscsO5GZZldp9gVCbxWRD5FqlplPhhYQTGWLt3budXXm8Av7d8YsDTHFdQu84M7u6rJTqjWKNtk3HDaFZRIOIc6ZXa2txFlq3NMClVfsRy0t/tcVdCuICucHVbe246oFc4tlfZBy117syfQzgoS2X1I3ZUVuL2pRoT+t7euFtWuFLtFWSmW6e1tY1eZl45v956q8PYGuaig1tLuMlsJvDNYgyto+7Gu7XRXUPwEPrCCqvPhMz0A6B0PscuX8089dkZEdt5Rcf355l0PjYhIvXE21rpXiojIit+KP7P+Z3j/vuqm7mU33RF6x53PfketMr+hLpIdTnC8Aev+vaLMT++7Yc8ZGdmxNbC4YRH5FWlquiwizz31pYACf/ep/xywVLXu0/tu+MIb8ss+ZXl63w17Xgosbf4qqNWc5Hor/XjvgZXTF1aOeJrWmxMOx66gRMKpdMpsL/qpiMhb3y5izdu84fjKQXv7A1cVtDuEN71dROQnP1gWkZFbb+mK6LL37BTx9jm5aG//7WynzM7+7eYbW5lwgdqbqGrHphgR+t/ermq6WlS7gv6t1aJeqysjMqi9tSuoVeba7s033PXYsgztPH7CdQO5Sv8r6I6KiDzzQLvMszvfKiIi/yAi4grWybAKqjfOtjuEaBUUM5xWBb3Y6HH7AKCDBD5fXqmfEpHtozd3/X394LtF5MyJJ8/HWfed1vIfqdf79osiIlL5o/vWexeeq9dFZPiqd8Yts4jc6h/Ot+Ou+2NPmRem7npMZOihz//OGv+yakTkF87gFdeKyPeXvxNQ4BeWzwQs9a5rl3nne5UlsZZu7L6wEy2cgIjSqKCFqbu+Zv/n3/9LYMEvKIvdQzhWBXmLJEm0tzdERF5eCC6bclGe2ltrqVWW7y+IyPAH9lxYOX3h5D0b/KKKFk5ARMm1t5v3Osoc1r/ZjG1v+0/o147kob3t2etuUe0K+qmIiFz20LOGt7d2BTkM75469uyRAzcrxtCIEWXXIbTKPPLrbxERkdd/JNIVbLi8VlD3ZEm/gsIEV9Dil58ggweQARL4XDn78vd8lmy6KuxJuAHrrg94HvPC1L6XRERGbr6pe9H5Zx7ZdcOeMyJD227zH/YC9rv2Mr+V7HDeeDXWuq07Sx1lfuaB+4/63ETgoBGRfzila4akdZVCVarIS9tl/tgVigpqLd11tc82c1hBVpmvu+GtvoUREfmn1n9sd58z6jWc0q+l1N5aX73ve8ssIj/6W/s/vKfA8tXeOkvXv1VE5HVRhdPy2rNHpetCXO7am72o3b89/XVPmaVw7c2hO9LctrdLRER+8j1V7bQY1N7aFdQq88cOn378s/fcFH5XTo4q6KbPdsr86+rbU9xMrKBWh6BdQRqCKqicxA4AQMdF/S4AnOw7wTb5DTTLL7wi4jNShq0rP/k7xR+tEU5ETj24bs2DqtWU96Pq7fdH/yQisnaNbzjf+Z7EWPf7P7X/492X2v/x9L57DouMTB26b73IK/Yf40UUdhh/ntRSZ5knPB/tLL304T3235b3bFu3x/PJ/FRQq8z/2zVf2LEk8g//6llnYWrN/Uc7//exe9Y85vlMD+H8XRrtbeFP95yx/3PtjmNnP+k4z9UVzsI9azardpuP9uYt88W/dez1vZ7Tdi1PLYiInDpww5oD7gW5aW9dftKY+9PHRGR4+/s32H8qWHvzUNSO5Ky9Obz81+KqHTfj2puInF6QgIgO37/usHJBXivo56oZQptpFbT8wrfnHuvqEJIQVkGLz9dFBhPcIQCocAV+VTtvTXmD1YPu28/e+f9ysuv2xfbN87p3/eUgouAyR4soB+GIs8zv8PvI2ZdlZGj4yuCfOOQknJazL8vlretTrz96z9Z9z5x1LBoZGu48acxXjiI6+7KMDF239iIRkX/6K1c42nIUjtMP/89PnWqfxRORwrW3lr/t/kO3fEXU9k/u2tGW03BE5PXCRRRLTsNZfLC7QwCA4iCBX83OfuPEKVE/Bnbl9IWVE0vPTu0cOXN0z7bbH8nNCH32Gydfdv7/83Nb7z8qsvO45yKA6jVy+YjIv8yBS72vkctHOBIWUcumew6cPPL4n1j3N1pplfNRwPkJx2HTPX9837vs/x7ZKKceu+c9+55pLTpw8sjjJ/+9/1PB8xfRpnsOnDzy+Tt+RURk7TpXOC12fri24nlKc/7CcfqxyMj2Y87f7hasvYmIyDMP/KeT1n+Z0mOLfHPhJyIiF19zTPXLalPbm4isrSgjsplTQcFMraDvezoEACgOEvi+GXAbr4nIuzYNi8ips35XxYIeFRO2rvzKr3X94dyTX1sWkZuv8llh/YZN9xz4/NSwyPKeI8/4fEjetakk1p11m9d1/o3XROTSi0VEXl/xDef6q33L7LPuuSe/9jfO//vI3k+dEtl96ED3E2XiRmQfxq5wNq9bM/5fRcR6mLX/QdZZ+s3AMkeJKC8VpC7zLwf/PueKnSMi8thnOjO/XsLZ94yI/Fry7c3l3dbzjZ1ldljrXZSL9uYb0cbf94Rzfm7r/XZ+ePuHPDdv56W9uZ3/L199Q0Rkza1LJ/1/EVCE9nZ+buvmew4HvJYih+3t/NzWzR///r+KiAzd7X3MipHt7ZHWbxwUZQ6WwwpyLu6eIZhdQZd/JLBDiCtsolW+ppT4PgHAo0AJ/GvfevTgxMS2tomJg49+67UeN+faXm8b1LHpcr8nlp194YzPEo11z3c/MldEOm9Ded+6wM2+f9uIiDxWe9p3v34nFc6+7nu07HAueYdvmdXr2mVu+ZH9fw/f75gZ3P8Va+GpB9et2ay4MhAckf9hfO35MyK+b3yzItJY+rNve8p8wxfsClres+2GPV1LOz/iXd6zTRFR/yvofFct2OG8/KpvmUVELn7v9iGxH+uQTDj1v0u8vTnWFRG59BZlmS0bN6oX9bm9qZbaHcIlrnDOLkxt3fapUz7rdArc9/bm/OvC1NZt06+LiMjVv7nBb+sixre3du2M7LjTb027wLlpb+0yKx9qaW57e1mxJIL8VFDLD36uXMfsCrruvm0b/LbeC/8Kqj+/mMYOAUClIAn8a986OPHRT3/x6y85rk689NLXv/jpj04cfDRyzv3atw5ObLM259reS1//4qc/um3i4LeSKLI03WZHRUTeWVKPhecb35WQZ70GrGtNWd95qfvv1ttQhkq/3kMQIiLv/O0REZGdx503182O2mUWka/5h/PewHi961plFvu2/1RYh7ErnNMXVqZ+5TkRuXL4+oACXzU8FLDUWvdr3/tJamVXy7KC1Jb3POx7fSYyZTh7b5LGi8/5FSliOGeP3d6+ymovarW3N94QETn8pQTDSbm9PVb7ojMcUXcIZ4/d/p77j56S4d337ZQeZdXeWmW2nqlvh3O2K9je9bW9WVb+n1btHLpwMuQ1maHhpN7erKWdFvXgQ2+z9lyU9rZWTB6AFEt/8DMREVlbpAoa+v2gyVIv/CZaVodQvvuDPMEOQAYKkcB/6+BHP/11nxsLX/r6Fz8aLeMO2pq1xU9vSyiH91pvXXI5+tUF15+fPvKpUyJSGgx6JK7/ut8XkdaPQDt/b71yKTiBt38nP1R6l98nfPZbs8p8ibpIdjjB8XrWbT8zX/6tiIhcWj3ZNcs5fWHlkH2FauTBCyunH/c+wCYkIr8iHZhcFJFrb/lQQIHffcu/C1hqr7tcdv828vj29gc9v3I/fWHl0E730u6I+l9Br7hqoR3OyEYRGd7/4E4R1RTt7x/fc0ZEdt5RSSYcu4KSaG/2NZbHak+3F1nt7fmH/8w6HbigOG1x6iVFOOERpd7eji7/vBOOODuE5x+2qmBYpt5zYFlkeP+Jxz97jbKU2uH4R5Rsh3B2wS7zvbee/7EVjoiIq+5czGxve86ISOnRR1u142ldXn1vb19d6NTO/hOP3/E33QOQc6mJ7e11aVVQLHmoIPfSo9Y3qH2BvggVFDxZ6kVIBV1B/g4gCwVI4L918NNft/9z442f/NwJy+c+98kbW4+H/vqn9RPurq21NufeoHz9WPTr+no23HbrsIgcvv/2BxbOiYjIuaeP3X7XYyIyvH9X8A+6Atb1Olevi4i8+/IN/hs89/Sx299zYFlEdlcDHk6m2u9MZewxERn+xO8FhxMUr2fdv3p8ydrj8L1XBB2I3iLyKdKcVeYPBVZQ7KXpheMTUSoVpArnvR/bPyQiR+/aNfW080b650+KyMjUxxw/m+8hnFYFJROO/K5d5tuvsxZZ7e3xR46KyOWXqcIR8YSjGVH67e2R74xstMr8h/sf7lSQFc7IRy49cv9Ra7Ie9rjm3LS3v/zTP7j/qMh1935EvvA1984rRWtva6SuVzuaEWXQv/3GB+wWdaj0DU+HcH7uD4xvb4UegApRQWGTpV4EVFB5emo0tf0CgFPTcK/++QN3WB7481cjLVRa+oy9wmeWgvfm8wEtW7Zs2bJli/fv9jXMpf1DiooamVpSXZjtutFOve6VQyLdD8U9tlvEuqjruAKsdqXr2dqdFUP3OzK1pBFO5HUv/8iSVWbFY34dV+ADjIREpN5v2S5zcERxll5p/zH4CryvvFWQFY59Bf7EhZUTVpF0xQ6nnE44ro9tP/ZsxHAkf+3NWbCHw77+Xnlrbx5bb4kQTa7b25XhLylUbTyv7S2e3Lc35yis1TnkrYKsH6HccuiC+44wXUZW0HbPQ/XVcyrlP58Kmq5rzfQAoHemX4F/7ZvfsO523/jhT+y4rHvpZTs+8WFrAvTSN76pc8n8W4v25fcby+9TLe9sUM6dT+t5dhvuO7J0fGrnSPsPQzv3H1rSexuKet0/iflY1LUiInLnn4Q/ytWz33J12i6zZ5GU7nWFs+G+I0vHdziK2InXu670/HCa0r2HQh9OqzqM8/UFu8zBFRRnaXEqSBnO+ps+e3rp+PZhdz0mGk6nghIN55K1lzv2OnLf0sm9N23SDcex9/y0t+5wZDnCDSA5aW9b14bs/e1/WJD2drAc8GMutb63t4NaLwTRYkp7i6TvFeRdevA3Ort+5qsF7BAS51NBE9w+DyArA81ms99l6MW3Dm779NdFZOOHPzfjTeBF5LVHJz76xZeCPpH0HsMNDw+LyPLyctffv/PDn/VauqSdn9u6rf7Hp52vB3vmgc33HA69v+76t72lX+EsTK25/6hsP7binVUowpG8RxQQjqyGCtILR3JZQbS3NtpbUmhveQ5HqKB8hyMxKkjP9W97S9df/GZ6ANA7w6/At6+Yb1jvk0tftn6D9R8vnX81kyIVyyv1UwEPpzFOwcKRwkVEODlXsIgIJ+cKFlHBwpHCRVSwcAAU2EX9LkAyNq5/h9+id6zfKBL5PkRfRs3XjQAAIABJREFU4acMCuTcI3NHR25dSutprlkrWDhSuIgIJ+cKFhHh5FzBIipYOFK4iAoWDoBCM/sK/Gvnz1n/EZBNty/BJ/Gj9fYz6jd++G7lj+Q7hv1ZH/iRW89lS975px4rPfR59W/vl/dsW7dm87qtx85lW6YA5x7ZtW7N5nVr7j+qXh4UjuQvorBwZFVVUMHCkfxFRHtzKlg4kr+IaG9d8hYRFRRd/id1AAqjIFfgs9F5xdyN9yTxc/q8W189ubffZUhQwcKRwkVEODlXsIgIJ+cKFlHBwpHCRVSwcAAUm9kPsWs/oe7GT574uN8V8QSeOicir33r4Efb6XvA3traV9r9PPnkk87/e+mll+bwIXax9fERNSkpWEQFC0cKFxHh5FzBIiKcnCtYRAULR0Suf9tbuq6633bbbcJD7ACkgyvwOtonCkRk44c/F569S2CvbeX2l156aTKlAwAAQP8wqQOQGRL4MK996+Cfffrrnew9kXfRAQAAAAAQjdkPsdN5QJ3Og+58vfboxEdb2fvGGz95guwdAAAAANAfBbkC/9L5V0XUufWr52O+Q8513/yNn/zEx99H8g4AAAAA6Bezr8DL+8o3Wv/hewm+fQE+4FXxqtXcv3qfIXsHAAAAAPSV4Qm8vGP9RhEReekb31Rm8K998xv2DfDv/03tFNyRvW/88Of6dN/8uaePTW3dvG6N9W/X1CML52Ku+1ut/9i8bqtrO+ee3nd7+2Nb98198dQzjzj+smbz7VvV+33mgc2ObR47p9jvwMBA175Cwgn4QFc4Gy5x7H34Q+1Pnnt6383XOhat+eNnzp7vJSL3fivjM64VgyOKslQdjoj81cQHHEvvmjKjgn6js+u3f+D2ib90fKyrgh74TGLhdFcQ7c1/Ke2N9hYSTmhEq7i9UUE5r6APXNeuha375p4+79iCp4I8JY/EU0G1Rm8bBAB9TcO9+ucP3GF54M9fjbTQz9Jn7HXu+MyS9kqRbNmyZcuWLd6/n76w0vp3fLuirkamllYcn/H7p1zXZeihZ088NKLfSIZ2Hnft4thuVakCyhwaTtR1u101OqT468W+n9eIKLjMiS91F69YFbSxWOEI7S3fFUR7o725l1JBoUvdxStSBQ3vPnRhRR1Oafch9SRK559yv+XpevhMDwB6Z3wC70y473jgM0uv2jn3q68ufeaBOzqpeOStRUj5owpL4J+dsl4hP7z/kDWALR1v/+VEyKDSWrc1dE0dfPgj7XWXjk/ttIexN4tzF461rvtEZxdLzx5qDXtDDz3bPTy7CtNd5mZ9vvWXT3wkJJyAeNvr3ltpB3Vxe91nD915pXuotrdwaGfrL28eeXDJUXLdiBRFmi539htYQXpL2zbsuO06O9gHHeE4K6gTTm4ryOnNrXWXHt5RWmP97ZddFfTsjrUJh+OoINob7Y32RntjAFpVFbT/0KP7h5xFtyrImvBcd8dVfhUUPqdS/vOvIEcKTwIPID0FSOBdKbySKn1XJ+qdS/bh4mf4IQn8kjUOdZ0btk/3bj8WOK4s7e9cCOiMTK51O6ei2x9o7fG+napd2GfTHeXxDs+eMjebzfZ+LwkOJzDeS0REduxozzmG959wh+OYW4w82Fr90J2d+YbiiIVGpCpSszlv7/dgYAVpLe2qo05ErqlSqzytcHJbQSIislZdQQ+66s4ujPXDl2u2JhaOs4Job7S3rsNLe+s6ILQ3KqhAFbT7ULsiAirIUR7rjxVlh6Dzz6eCqiIiUp0PmekBQO9M/w28iIi87+Of+/CNG9XLNt744c99/H2aG2r/Yr6fzj/12BkR2XlHxfXnm3c9NCIi9cbZ8HVFRHYfevy+9ap111f/15veLCJyydW32R84+8IZERm+6rbR3SLyWO1p10ZvumO7iMh3Xz4Xs8xvBIYTvu7Wnz66bP91+x/dt94dTmV0R2uVU8fnWgdnRURELhtRhKMRkU+RRq39PvfUlwIK/N2n/nPAUnvdlu1/ZNVRJ6LK6G4R++0QnQqywinltYJERHbsuLwdkauCfvePbrM+4Wxv1tds/ZZkwxmlvXUtpb2J0N6ihUN7616XCsp5Be28oyJnX/5eOyJ1BbWC7Vin7BA0+FXQ1HRZRJ57kZ/CA0hdIRJ4kct2fHzmc5/88I0bHWn8xo03fviTn5v5eIRH0MV+5VySXqmfEpHtozd3/X394LtF5MyJJ88rVnKtKyJDD006hxb3uv/yqz8XkZF7P7bJtfLyC6+86aohEfle3b2Lm/deWDl94eQ9G2KWWURu9Q/n22HrbpGnxD7RPrJpkyecTf/2EhGRy+5eWjlSdUf0mmxUhBMeUXA431/+TsDSF5bPhK7bFY4rok1XDYnIv4qiguo5rSARGXroxuay+FTQ2y8REbn897rCEfnZL+UxHNqbjfaWSTi0N1te2xsVZMtrBd06erPIfzvr2yFYFXT5r3b3ByK/rgwnnF84g1dcKyKLX36CDB5A2gryHngRkcvet+Pj79vxcc1Pv+/jJ054Pqv8Y8bsE8kKm64aEjnjs7Br3dKge7ByrfuuTcMiy6fOnhXZICIiN01ODR8+sHz4fuunosv/7ysiXeeq45Z57WUi6jf82UV649WwdX/+iojIZSV5o94qszOcsz99Q0Tktdfa4YjI263/OfWSiCw/9uS52/5gg3f0jhHOVUMiZ/4hgaWucMQRkXU3hIiIY6kdTk4rSERKg7/lalSKCnr5e472tmPt4Udfl4VP/8cchkN7o71lGQ7tLeftjQrKeQWJSPesJrhDaPvBC2dEZPmFxMIpXVMWWYywKQCIqSBX4AvEdSJZYfmFV0LW9V/dXnfT5VeLuO4c23TP48e3D4vI6yIi8uj9rTfEHHsm6I59jTL/6J9ERNau8Q3nO98LWfet/1wXkZEr3t1VZjuchdqj1v9b8NwIt+6t1v+eeuSG92yOEFFYFfy8x6WiDkdEll94sna4/f+6lm4sSS4ryFrqbVQBFbTpd+zbnHMYDu3NRnvLJBzamy2v7Y0KsuW1gqx1I3UItgt/fVjiCKugxefrsbYLAPpI4Fcj+3duR+/aNdV+S+q7brza/Z6V5VNnju45cM97Nq/busv5MtU+2fjb3jKvPD+19f6jrU+4FomIXPix9b+tJ1DnKSJVOCLy+CN2OFd6l779nbmuIFWjCqkgl5yFQ3ujvWWJ9pbz9kYF5byCYnQICyezLCAAJIkEflW66bPWs+jPHL1r27o1m9et2bzuPfcfle0PtZ6tuvTsiWP7p3aODImInDrzqbu23f5In0fo33SU+YY9Z0REHn2kU+a14gjHGrM37rQW3X7owsrpvEX0m+4qsCOSih1O+UHHUiuchZP5rqCbYldQLsOhvdHeskR7y3l7o4JyXkHxOwQAMA8JfN8MeOx7Ruyfclk/P1Mavuqdvpu01vVf3bHu+urJE8f2b2+9HWdoePehpZN7b2l9bMOm9Tfdd8+Bk0curJw4tntIRJb3HHnGf78lETl14AbrXMCazevWtMK59GIRkddXfMO5/mrfeK11f/xvSvYHusosbx55sFPm/zDrWLRxrYjIe9/riDpCRHYVOMOxI/qmtV+/AmsuVYfzZhG57j/saVXBe7uCFakczGcFdZbGr6Cew9m8bs14LZFwaG822ltAOLQ3/6XqcIxtb1SQLa8V1Fm3pwqKIGySVr6mFG2DABAZCXze2D/lUug8XSZ83e63zXWt+8wDm9et2VuTXY/b71M9s/zdl9sfu7rkfKDL+ps+u+8hn9fhtPfrNwCefV39fJpOkS55h2+89rpvfmcnovU33bf38dZrZq/e/t4Nre38ysnxe/bUr7bfavv210VE6s8rwtGIyL8KrGfe/HJgRHpLXeFcWDl9bPfPReTfdMLZ5qgg64UCK5LTChJHe/OtoOU9Dzva20vWTyl/kMdwaG+0tyzDob3lvL1RQTmvICffCrq6NNIO9sLKv7ceiqHqEDT4V1D9eZ5gByAbJPB90/TYe5OIyDtL6rHwfOO7IjK07baAwcZaV0TOfGp6wX/dhdphETkj1rhln07+2lNnrb8Pld4VNZZ3/vaIiMjO49boePrCihWOtV8R+Zp/OO8NjFdE/nrNvUPuiJzhWGXe+JOnnBH9+loRkZf+a9xwSt3hWBHtest3ReTK4esDCnzV8FDAUmtd8a+gV/zDee3bOa2ggHDaFSTd7U2k9QyhRMI5fWFldpT21r2U9kZ7U4RDe6OCClRBAeu2K0hZ5vgdgk+RGi8+JyLluz84GHWLABARCXzurL9l+5CIHP3qguvPTx/51Cnxvh9Oua6IyOH7O79Jc617/pkH7Me6rFhLN71/24iInPnUtj85KiIjt97StQt79YBxLrjMlwSGE77uV356xbCIHL5/3ZrNU0+7wjn3yNxREbnopWURuXLqY9Z7WTdde7mIyMqLynDCI/IpUs1a69pbPhRQ4Hff8u8CltrrijMcx9FY8+TcURFZ+5L1NoFWBVnhvHE0rxVkhfMb125WVdD/0X6GkLu9ich3TiYbTo321rWU9kZ7ixoO7a17XSoo5xV09KsLIufntio7hLmjIrLmzKfe0wq24wV1hxDOr4IOTC6KyLVXkL8DSB0JfP5suO1Wa7pw+wML50RE5NzTx26/6zERGd6/6yaddUVEZHnPttu37vvSFx9ur7vp6WNTW7fdc1isH7x95a59z5wVkfXVz3+kJCIvvyYipVve39nc2fPPPLLPWl12V6v+45y3zI1aq8yf+L3gcILitdZ99NF/Htlo7ejoXR/YYq97izyyy35Wzb+KiMj3z7Z/lPYr7ZL95GsPP33+XMSIVEWaqYzZZf5QYAVpLW05etddn/pPdgWV7nju41Y4r4u7gi61b9jLbQWJiMj5l62I/sf3tyrohxO337Dn+yLiaW+3vVXEeqVRUuF0Koj2RnujvQVGRHujglrhFKKCDt9/+wOvXGMl1Xfter+ngu78fWvRvtZL7370tyIib4jIzj++Z4NvqSOEc+7pmcrYnIiUp6dGo28QAKLy3siNtG3ZsmXLli3ev3ful1tSPhx1ZGppxXlP3aGdItJ9o53Pui5DO48f2jkS9imHtbcccu7a+oHZsPV7P40yh4YTed1uG0tXKv56seJvtuHdIREFlzn5pS4Fq6CrihWO0N7yXUG0N9qbq1RUUPhSl0JV0PD+ExdW1OGU3CX0m1Mp/6n3W56uh8/0AKB3XIHPpQ33HVk6PuUYcoZ27j+0dFLrVLFnXYeRoZ37Dy2tHDlwc+XAyRPHdg+1T8YPj2w/9uzs0vHWi2Fs1sNa5bY/rITuOqDM3iKV7nWFs+G+I0vHdzie3Bqw7kUXOfd6/VU79x9aWjn+jWVXOGtFRG79wsoJT0QiIlsfPvH4Z0Mi8uy3XJ3ulDm4giIu9YajqCCRjZ8wo4Ic1lyy9RMPLq18yRvOWhGR9x9MMpxOBdHeaG+0t+CIaG9UkEIRKujNa1v/NTyy/dizJx6/b72IskO49X+/L+Lj64LCKVen5+sLE9w+DyATA81ms99lWHWGh4dFZHl5uevv3/nhz/pRnBDn57Zuq//x6QM3d/70zAOb7zksw/utoVHp+re9pY/hLEytuf+obD+2stfziwNFOJL3iALCkdVQQRrhSF4riPZmob0liPaW53CECsp3OBKjgjRc/7a3dP3Fb6YHAL3jCjxCvFI/FeMxrblVsHCkcBERTs4VLCLCybmCRVSwcKRwERUsHABFdVH4R7CanXtk7ujIrUuRH9OaUwULRwoXEeHkXMEiIpycK1hEBQtHChdRwcIBUFxcgUeQ8089Vnro8+rf3i/v2bZuzeZ1W4+dy7ZMAc49smvdms3r1tx/VL08KBzJX0Rh4ciqqqCChSP5i4j25lSwcCR/EdHeuuQtIioIAPKKK/AIsr56cm+/y5CggoUjhYuIcHKuYBERTs4VLKKChSOFi6hg4QAoMB5i1wdmPcQunv4+oiYNBYuoYOFI4SIinJwrWESEk3MFi6hg4QgPsQOQLW6hBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGAAEngAAAAAAAxAAg8AAAAAgAFI4AEAAAAAMAAJPAAAAAAABiCBBwAAAADAACTwAAAAAAAYgAQeAAAAAAADkMADAAAAAGCAgWaz2e8yrDrDw8Misry83O+CAAAAIGHM9ACk56J+FwAd/8PvHuh3ERLzj3859Z0f/qzfpUjS9W97S5EiKlg4UriICCfnChYR4eRcwSIqWDgicv3b3tLvIgBYRbiFHgAAAAAAA5DAAwAAAABgABJ4AAAAAAAMQAIPAAAAAIABSOABAAAAADAACTwAAAAAAAYggQcAAAAAwAAk8AAAAAAAGIAEHgAAAAAAA5DAAwAAAABgABJ4AAAAAAAMQAIPAAAAAIABSOABAAAAADAACTwAAAAAAAYggQcAAAAAwAAk8AAAAAAAGIAEHgAAAAAAA5DAAwAAAABgABJ4AAAAAAAMQAIPAAAAAIABSOABAAAAADAACTwAAAAAAAYggQcAAAAAwAAk8AAAAAAAGIAEHgAAAAAAA5DAAwAAAABgABJ4AAAAAAAMQAIPAAAAAIABSOABAAAAADAACTwAAAAAAAYggQcAAAAAwAAk8AAAAAAAGIAEHgAAAAAAA5DAAwAAAABgABJ4AAAAAAAMQAIPAAAAAIABSOABAAAAADAACTwAAAAAAAYggQcAAAAAwAAk8AAAAAAAGIAEHgAAAAAAA5DAAwAAAABgABJ4AAAAAAAMQAIPAAAAAIABSOABAAAAADAACTwAAAAAAAYggQcAAAAAwAAk8AAAAAAAGIAEHgAAAAAAA5DAAwAAAABgABJ4AAAAAAAMQAIPAAAAAIABSOABAAAAADAACTwAAAAAAAYggQcAAAAAwAAk8AAAAAAAGIAEHgAAAAAAA5DAAwAAAABgABJ4AAAAAABM0ETmtmzZsmXLFu/fL972Z/a/iRMzS2+0/15f+uZHJ/6sszTk356LflWv7n/v/2o2m83mfNXvA796+S9ee++burZ/ww2OT1x+0QfUZW42m+feWDl9YeX0hZXTS8endo601xnauf/QUmtR979//Ptms/mP/9j5y9LxqZ1DcVu3x/DI9oeOn+ja6bHdjk+MTC2pyzxfb7pL5R9RyNKHb1qbeTieUpWr04d8FikqKOAD+uH83r0hFdlDOK4K6ikc2lsS4dDeaG9ZhpNie6OCkggn3QrqLLqkE5qrbCeO7d9+3eXOslcOuncR9Z+qgjRnegDQOxL4PghL4E/UFCu9MaObw2sn8NX5ZrMZlMBbfvWON4Un8Moy//cfvLFy+sLx7YqNOsZ1x79z/9hsNp0JvHLdng3vd805FBMO5X7LrTIHRxRjaW/CwwkuVWgFRV3XR2hCZYkfTjnTcPTR3lxLaW9Ce4vAlPamjwpyLU2zgoZ3H7qwcsiRaTu3sP1Y7AReXUHT7hyeBB5Aekjg+yAwgf/CzKvNZrPZfLV11X3iRPsv79e9CP+Fjy61Nrp04uJt9/6iiMgNrTy8axfW0uq8ZxdvuuGOX7QGpg33difwrqxeUWZ77//y/P88bI2jrVPmS8enWn9xn7n/uZ29N9sJ/LNTnXV//oN/aTbr89O/af3l3lvbZf6Xf/6eNWPYebx1FsA6JbH7kGfcPXFsvz2nb324M+HolMe5X2+ZE1p6cLeIyJ177b90FdgR0aGdnqXRwlFHNF22/vKJj4RUUEBE7nWt/XYugCgKfPrCyomHrrSXl7oaQE/hqIvUSzjORetbUXU32pUTD7WnhiMP5rm9dS11hrNkFXJk+7FnHe3NU+xctbdHrZJcOVRql/WWirLMprc3d+2cvrBy6E517eSovV13x0arzAcfdrU3q2AFaG/2ACQiIltvEb8K8vSBeamg0oj4VZDsPlSECtp/YqmVY0epIL1//kVyp/Ak8ADSw2/gc2bj1WOXiUj9vvu+cfglERF56YU99331SRG57Ib/5Xd0tnDVXzyy+5H3RdmFiMjLy55d/MKvvf+XrHT93PP/X8Qyi8iPReTcU//3sojsPvT4fZUNIiKy4eZ7Hj++XUSW9xx5prWFX7powyW/fPnF7s2ee/Jr7XVLF731IpHBm+/8C2vdL7woIvLffyYiF73pb546LCJDpXcFlNGy/qb7jljTi6NfXfD7kHO/jjJXrTJ/Sb10e8Sl8u3DIjL03t+3l7ot1LQi0grHJ6KJhfntIrL8Z38RXEE+R8O7rl3m/+nhoEsi5x7Z+6nvpxFOq4L+f/buP7qpOt8X/rtQmaJ0FAHtDCJtTRBrrTCU2kkOMwqiJEhhgMtz7PNgL86d5Fh5SJxZtXO1j9fFqc6U3iOpC/A2HmVV7uA9HEAskqAM6DmcxAI9g3Y6WEmmFLFjVYo6VegokOePvZPsJHvv/GybhvdrsRYlO3vvzyffL2k+2d/9/aYsnZBNZz5XTueoSqTp09+CWzc9PCn0/K7NtR1AyfoX6+YXSh4uK4gIO336G14XYt7Rcjjw32fqg0LhIY1t9Pe3P0S2Tq5/W0TYadLfqmftPSXE/OBDkvc3sUe9mAH9TfwFVFYA4OBbUGogbH078Dsu9oyGvIFWrrz2KGQaSPgKbOurT2ZAA9W2dC+sE94QFBqoAIhsoJioNJDb2uiM/4BERPFjAZ9e5s6boQFwpGtryMNdz+46B0Bz8+Qo+9+97IJtyaKp8B7Z+5ONnnhO8dUO2VPcUDQ2wZjR981l774dxwCsXqIP2bKwan0ZAI+3GwBycmZdn3NdNgYHB7u+uhh40pm3dnX49835XnYOgMGLX/j3/SsAXP7ym8uA9/QJAGX331eIWMxfoj7oTnpeacw1Nh2Azrdeld0qRPWnt36nslWyb/fpDwIxL6wSLxIO+J8v3ZpsOsoZGYSo+pUD9ni7lV6NiH0DMT8UkU7Agfry2g7cWjAk6YgNlKJ0gr4GAFwjE4iQTtnKBarhpkV/k8b8cj+ulj5b3zhwvG+gxRTW2a77YULpKGeUyv4miVny3ydfqw3ZKxP624/kW0dZGvS3X4TEHGigcUKPKhr1/S3wCygQs+J7tfg7Lp6Mhr6BZvrapTEHGuj7d64GgIGfvp8BDeTxdkveEGQa6IcLANkGSjAdfwOd9MZ7QCKiBLCATy/FN10PYL+7K+zxYx/1A9CU3zY36iF6Pc9v3HrHs13H4jzFSdlTnP8cACaqTXqjdMDBi5c9J9wAVhkWhm2aprkdQEfr/jPCvy9e7PvqfNdXFwclz/nIcxTACsNCAOPHjgHw5d8uBvbtBwB8e/Ey4PEcBXA7urfUP5A7Oy83f7zRrhJuj8cDoHTmzQrbpeeV0swoBvBh+3uyW4Wouto7VLZK9v1zd3sw5grxIuG/7e0Rnh6yde024UH3Oz2JpKOSkRAVgPuVG+gPse4bjLllm5DOzrUPrNsuidlVs3wXULJ+nfwHwaTTERooRekEY34TAPBNRBj+dF68Ozdim1Ra9LfQmFf/SDViwWmXbNjp0t+km4RYPjp7aO8uSWwZ1t9kyIadTv3Nv1WI5UP5HiUaRf0t8AtIiLmhte9gZb5yfGFGvoFq6/oGjktiDvwP+qta3KOsgTpa958R3xDibKBo1BvIvWMfK3giGgYs4NPK5Jk3KWw53R/Tb4V39oyv3lP7ztmUnOLyZ4e/+/3eS8DYqUXKHUX5gJc8f+pU2FQ4Mzi/1ODge/2DfYNhg/SFK7qCMd/LVtj30uVB78lOANjaWFm7q116hK3/ZA//cv3MoS1V5bUdQEnFomnykUnPG0pbVALgvMJWIaoYtwofesJj/vzN8twqe7fC1g93ClvjS0c1o8IpU5V2El/k/o9j3Fcac2DkR/vWxkDMh9at3QaUNtSbfqB0zmTT0d6QsnQEQsx3LJmptKm0oV710mi69LewmB+dMUl2rxAfAljxy2pp2GnU30I2TZsEAKf+uXJrMOYM628SZ/8i/qAPbR2kW38Lbp12HQB8jogeJTGK+lvgF5BMzKHCL/ymbQNNAoCvPtgmE7PfaGugr3bVSN8QIvzlIOIYuBek1kC6OI9FRJSw7OhPoeEzecZUAOdOnlbYPnVSMaB0aT25U7R92/RjbZPMDmOLa68qVPnArxKzp8sN4MczlX5Ftnd9BCj84heu6JYVFgIYk5MN4PLgJbknClf5AZQ2bNpUrc/HxLP/Uln839zAqafunP2UzB4l699XHpIqPa+cb1KxtburQxozLPeWv9yPHGCw46lfbM+9XboVmwMX4dEhl5FqOuoZnR0EgCm5ig303geIbV9pRr/+8H+sfLkfBX+3esp/bDva8dQvtt/36+7KrUBZzabqaTggHsBTW5FXG3ncJNL5LGXpAMCBeiHm//F3b67cG/pk/6ZN1dOAj8QHjz6dl/u03GlHvr+Fx2xR2AGummBnA7CrMndXxHPSor9JY/7ty/0A8PnHKKvZfrByPkKTzYD+JgprHVemvaBqAAAgAElEQVRl7my5p6VHf4uMOefvtn9eN19pKzBq+lut+F6HKSu3dz85P/wJEkcby3MbIx5NqwaSOP2fAEpXzMsPPjSKG8hz9MPgG4KMUwhPNjbRGsh9wgNo4j0qEVGceAWeovladQa7kfZn4QJ8aUNrYEaZW35YrL6Pxz90f4Sc8f4JCI0ZAKYvXg3gaOPv3HJblY10OgjLSByRMa6k8eAmIaPV4mjzmMYxpkE6kIyQr4wYYBLclB/bsdIgo9hi7j6NsoKoa0SnQToS3acxxf899NHGygX1h7rja6D0SEelv/l1n0ZZSWmZ2i39gvTICIAY8x1CAw3+R+WC+kPdYVtHYX+b7p+h4POd4RkBAP4S/kC49MooYDDwpaTfaG8g8Q0h/CliA4UlS0Q0mrCAJ0H5OMu7Hp/P5zv5SGAZuXtrs/OnX+rZ++3hw2lbw99icfl83/W8Efmb+FYgfN2v1rb3a1aXdWyrrXhgywh+5phmOni8b+B4RMzTDcIKug+3ymxdsylieZ40SQfKGemFjDzA6t3hV2kil/VKm3TO2Bes3SYXM/CJ4ia5ZeTSIyOVdEIVVjYe/NUi4WexUJQulZwm6YQqrPz1kmsB4Nb/Z3tDCY7uqrxTPtnR2d8kCisbD7a8cfBXkmW9whayTp+M/AorGw+2vCg00JQ8HN1VeWf9oZCto7C/VftXBykrCM8IOLTunw4KP8kt/JmOGQHvur4CgJyi7WH3io/qBlrztP8NQbaBCh5P5Y3xRETDjAV8Wjl7shfA9TOmK2zv7Ve6qXwoTjHm6knZJX+fPRH4Yu+lz5QP+L/NWVmTLE2PD7YG/5idALQzdQDe7VKa51Vt5ptbCksBHO3uBnB58CKAMTlKE+JfvHxB5tFrCyCO0g+Yll9Y2fhiTWnoInZy520sz52dF/LH/G8AhOmhhajkJLMV194g3LwXGnNQxNYY0gFwS6FWJiOzE8DkHAD4fECxgWbdFmyFMNH2xbU3ABh39hoAuLW6MXzKn0jJpFN/CMANqUlHXBxuzabImP/2HxuVNiWY0dD3t3eV01FzuzCZ867ngh/N07y/aeZX11uF97WypzOjv6mZgtDWiS2jEXx/K/gvET1KYtT1N5mYz9gXzK6UrM0aIQ0b6Ix9wezHPrwIACWr1G4HGG0NVDrzR/Or65Ub6EezVJJVIf1kIkdXpFXYQkSUQhlUwPce2bnRYqkIsFg27jzSmz7Hi8XZro8VtkyflKK7quI9xaQxU6cDuNQXPsl88IBKU+aN1d6uNJY9cMu0osLptwV+vvy3i5It0n3HjglbPD4GhfMqygDsch6Q3So5b6jeEx1A6ApcEVEluPVMv1Kw0amnA6BwutLXJN2fK/Zn8UWedJPSq6G2bzCdM66ObwDgwy3Bj27LxRWDPbUVebmzwy/dJJGO57OUpCOuEoSta4WAy2uD/e2Pe7ukm/JyZ+flrt0jbDv6tEw6UTMa8v729R9C08nLnV3+sthA7bJNIJp83wq5r5PSur8hS/jr6NOZ0d/UGqgg8gtKIeC0fX+bJN+jRKOnvwV/AUli7nbVLKh46ihQtnKZ0p5iwGnTQIGYr1OPWDAaG2haIg2kTrmBAtPxEBENvQwp4HuPbLQ88swrb5+SfPl96tTbrzzziGXjzgSK7t6dlgr541VYNh5JPl5FnR+fA7BIFz4P8dybJwHwtn2Q3Ax2aqeYkegpOqf9xufz+dpac/xj73MqNjQbkJM9RpiWdUfEr3bhlmnVqWtxszb4seDCpcsArvtedmBfYVa9cdmnmvRZWVd9r07m6/+vTiHK8jYq58Xq3WGjH2uu7QRwa+ksyH1YEaKaWVqistW/7yvbHwhcxBO2Cp+Ibp4sfPK4ZutaydYgYWsCGf1UJqNmg//GdeBN5Qb6kVb+w1novt3SjILpADirNPFx4mTTqZsPcTmCFKQzrIa6v735wVdRIvigtkK2vyVqJPvbEBj5/tZeuzmFrTOC72/iAwrvbwlnNNz9LfgLyG/gnQfuXLvtKErXbOo7GGVdyajpDHkDCVu7t/tjfnr9jcKZJ4uPhzRf8kakgUI/XaS+gSJDEt4QdKsWcwY7IhoGGVHAH9n4yDNvK4xbO/X2K4/EWXL37rQ88oriMLhTbz9TMXQ1/LHDJ70A7pq5JuThmU+suB6A9yOV9eGSPMW1q+RP0X+59zSAsRMUp7JROCDyrhmjWbxqLoBte10hWw60PHUUgFajdkeu+N35tr0uYPBvFwcB5GRP9O/7fQAYc901WmExZrmv/z+E/HcE3YdbjwIo0d4SsUv4eaUxN1rdAIrve1B2qxDV7ff93ypb/fu2f3ObNOYDLeI68Hjnua0ACkK2BgwIWyMyipKOckZOIapJygFrNYVKr0bovuJFiV3OA5J0coHuwx98DqBk/fuSj2679cIxhHuSw2+bTzgdsYGST+cj4X5+f7QrAADizGGl4fdRH+8b2CRezyl7Wm4WgJHvb+260HtxxYwC6WxaDsj9DzqxubYDwOol+njSUc5oGPobPjp7GggrFUZ3f3t6NSBf5x89hcjWiZ7RyL2/5Yo9SivdGjR6+lvgF5A/Zu3One3CFJ7PRzRHpBFvoL0udLtq7mwUY17yR8n/IIQkG2J0NZDw6cKVSANFEaWBZrB+J6LhkPoC3us0m53emBYtT40jG595W/yx4J4nX2gVvPDCk/f4p+t9+5k4Ku7enRv81XvBPQ8Fjtf6wgsPBQ+4PZHL+jE59YGjF4B2y5Zla4TTFcxs2LJkEYDetmffGZpTAMD00shTnO+6eLjh4hcAyseqrCQnFzOEYXn5991bCmDr2gfWuXoAAD0Htj+wfBeA0oYqtTvugPxF9wf29Vz88iLgPbDnZ8K+D88AgLETAMNj/3M2gG3Lq2oOiANN/3zs38VDrDGFTQrVc2D7A3c2tsttkj2vJGa7EPOD8lt3xbN1y3tlBULM/9CwWdgKADt3tgNY86tNDSVhGUm2hoQdSzoKGTXpjbsAlD7+M/UGUng1wvbFUjHmB+4IpNP7aiyxSSWRjr+BUpNOxCahv8UvPfqbzFYJ/aOy/e2NLdsAlNU8KrkxO63728CJmgVrI2NWMRr624/kW0cQkWl69DeF9zd/j/rN6O9vYgMJMefCIy7AEX1K8/RooLVz7127DShtaN2kPZyRbwilDVXzu13iG0I8DRQLlQbS2WoMKTkHEVE0vhTz2HTikXU2T6oPLuPjf123RLDuXz+Oa2OU48nu0fZcvMeTM2fOnDlz5kQ+Lo4/F2eDD9PfZNkgHaP+SJsQUcjA9ZA/z530P+HhsQBQPi6wKeQUDlOUPjJzbLnksOXlADBxyTjpueRjvvRJ/8DxtoYSmWOW1bSFXsYUplb+r3t9Pp/vwgXxcfl9dTaPELPJ4bv0Sf9A6/qyOHt9WcjszRGzuyudV4xZPaNEtoY/Lc6Mbo2SjnpUURso7n0VjhN2RTT16eiGJp2ymjb/VWu1K/Aq0q2/3VoSmk7r9jUyz1KU1v1txfb3Qxto1Pe3OFsH6dffpIG9n1n97dboq/rJHTxdGwgAsHr3FdRAktE64oeQiJsXZP4ofjKJ6ZMeEVHyUn4FPjCPx/DcCtT77mHhcnnBQ4+vnBq+derKxx8S3r5PHX43pmvmgePdUxl5OOCuVf7jnVGaCi55pw7fYd37/JFzgQe8R9qqrS/Vqkxum/Qp5N2h05lsjp0brrohkQOePne+7zsgv7qlbXfN6mBFqvt546a22FZwidi39OcbHR6XRexZlz89d77vO0wzHTzetntFaeBpRTeqHFP78Ka2g3VRLv6Hn7dkdYPD4xJjltsazCjOrRL3Pd0nPi0iIwXCbQ3LfhMlHbnz6kw2MarIkLQPhzRQfnVL2+6Vkql1gxlF7DtpSmB5gykFq3e39sWzVE8S6QQbKKXphLRdYtKsv5WsbtjU9puweZKnzX8+I/pbw6a2g3XzYxvukVw6w9nfYm0dydnTp79JlFX7Wycj+huAsuqNurh/LY94A22MYYGGTGigspLVDZsSaKBYKDSQhcPniWi4ZPl8vpQe0GnOMtoBwOTwNQ/9YKIjGyueeRtAwUMvNMlV3MEb2hWfkdD5cM+TrY/dleBBSktLAbS3t4c9Pn5pY1LBDYG5D/38329yjX9WMgX9Z7sH29owccm4efNUvv658HrNe59+PfQByph47YTpOfjyq697BsM3nbEvqPD8+njYck2H1s2u3Bp1iN2sGyeMSEaumty127Bi+4DshySZjNI7HahmlHA6SMsGYn8LYH9LFfa3dE4HbKD0TgcJNFBsZt04IewRpU96RETJS/kVeGHecQCdJ4fhPvgjbvH29/xpCrX51Gn5wg8puWYeON89ukSr99Gl+KbrvR+nYOa8NPGR56jKXDujUYZlxHTSXIZlxHTSXIZllGHpIOMyyrB0iCiDpbyA1yxeJVTwbmujc9imsiuYdpPSppumJXSTWrjeIzs3WsSr77jnyYSvvo8qBfN+edc5x+FMKeB7tti3ld1/X8wjbNNfhmXEdNJchmXEdNJchmWUYekg4zLKsHSIKKNlp/yIGovLA73W6obdqO002eqWLdYaNENza1DvmR7hB8UL8OIl+FMAes704q64x9AHxsz7Fdzz5OOxlO/C6CkVZ8+GFMaTJw/RMsaJmztvhnfX6/L33n+x99vWvcD07Hsfzb56uAOTd9W4mdePy1HefuatXdr1Lyrez9xeW5FXC5TVJHnPc6r0bKkqr+1QfYpaRumWDqJndAWlg/TLiP1NKsPSQfplxP4WJt0yYgPFL+xDHRHR0El5Ae/1Oj2eGXU2U73V7obbbjXareImnU6nuiuK61zDcNt8cgoAoBdI+m76UeDYKy/9bKRjSKFppoN1Ix1DamVYRkwnzWVYRkwnzWVYRhmWDjIuowxLh4gy29BNYpeAuOe9C8xQpzalXNR57qKcYcNhIB/oOXUqeCk6hmNFvQK/f/9+6T8nT56chpPYJWwEJ7EbIiM46c5QyLB0kHEZMZ00l2EZMZ00l2EZZVg6AGbdOCHsCvyiRYvASeyIaGikfgh9Zpm6sqlpZeBfvUc2bnjm7VPAqVce2ThNfRp6lXdtobZPwzHzRERERBQvfqgjomGT8gJeW+NwLEt0X23054yoqXc91vQkhAv6b2/fuequ5NelIyIiIiIiIopFygt4jWaoZqyTEcsEdbFMdBePu1Y9VPD2K6eAU4ff7V3JCp6IiIiIiIiGRcqXkRsZKou8f3xGdhb1xAVWliciIiIiIiIaNqO8gL9Ld4/wQ8+ZXvlnBC7AqywVT0RERERERJTuRnkBj5umFQAQxrPLbe9997BwAb5g3o9jGO3eu9NSUVFRUVFh2anwhQBwxC2uC5+iMfkKCmY2PPHzC6/XCH/++MS8NQVxH+Py+fNVeXt+1Pr4oPhn83cdhy+fF7fOvXvea1tq/Kf4+WsPXVv82eHvDm8eDDz/cMjzgz7bPSg55sXAEyQxA5h57biJV/k39RzYXrNgdl6u8KeqZourRynq7PwbJ8y6VvbmjgNP5OXOvnvuo/7jzM5b+LD/UGcObalfEnj8/hf+z2FfTveZQ1vqHwied/YDC+RPfWhd8Dl5C7b3yMSsNzeF7BhrRgfq83Jn1xyQ3Xb01RX3Bs+bOzuv9EHJcc68WindurzmlaPJpBMac1ZWVsi+cTSQYkZnehoeDEnnh/c+YHk9kM6hLfULpVtz1z2XsnTCGygV6QAH1uXlzs6Xb6DIdH59iP2N/Y39LSjD+hsbKP0b6Nd5ubPvCJ663n7gjH+bXAMpnSI2EQ3k9CZ3QCKi2PmGmMfjsJlMkiXgTQ5xi8OkM9kcniSP//G/rlsiWPevH8e1Mf7DhWxf8lxbojHPmTNnzpw5kY/nVGzw/2l1yuzX32TZIHmO6p97Hx47UbHRxxbXztvZH3pwh0mn/Pz8h0MOXl4u2Tg9+16VmC990j9wvG/3CpmjltW0DRzvC//Tc8Hn8/l8Fy6Eb2rbPe9GxQhnGkpkHs1VfH7J6t0hB9++JiIw9ZhjzWjTagBA2On6Bo637b5/mnJ4699vXV+muDmRdNRjjqOB5DNq271CednEglGXTt/A8bbdc65RjFC+v+UkkxH7G/ubYoTsb2ygmJO9Uhuob+Bp2feE0jWb+gbk09Gu2RRxkJj/yMass0k+0ip90iMiSt5QXoH3Npn1WVqt0Wq3u91umSe47VajNkvflMy3llNXVoqj6E+98ohl45Fe8cp5b++RjeIi8QDuqYxxtrmpP55XIHc4AL1HdkqOWPDQKrVF5JIyuWHLkkUAetuqrY3jlzaOt+59vhfA9etq5s2N6Qhd3/3+5UtfADqTzeHxtLUGqvrs/OkALnU2fP3xOeDc/o1bxy9tHL/011f90Gh3AzqTY2ez5FuA2nHF0wFc6nn5u+7+8JNMXDKuYkNOxaPZV8vEDKDvIoAxed8/s+OB5bsAlDZsahM/OtSUAjjauHbLmZAjjpl47YTp8h96XllbvvzwpwCAhVtahd+g/Re+8DhsJh2ALmcHULJi+/vCL9eezpd0AAaAKfdtantf8qnl/U3rywB0bFteZe8OP0lpQ2vfwPG+g5X53dsjYrbpAjHLbJXLqNtVk7t2m2w6B+rLl78ZeKrwWaQt+IGg46k7H3zqKFAmZCR+ZBEs2xx/OkBEzD6Pwx/zP26OtYGUMjpQX758V2DZxAX+CHfWCZ9qT4Wmc7zv/ZWTUpyOpIGSTwdCf/vPbwAAs55u9X/kDTRQWH/bJCy8Mcj+xv7G/oaM629gAwWlaQN1ux7JfVp4Twi8IQgZtW9d+w+WupA3BH8DebauVRibEI1SA7mtVUl9oCUiitFQFfDeJn2W1mqXK9vDua3aLLMz8VPd9diTYgmPU28/88gjwhj4Rx555m3/7HX3PBm5YvuRjRWyg+Wnrnxc7nAVFRWPPPOK5IhNQzf/fMFtxqkAPNXVh7cKJzzVVVu9dz+AqeVP3B11//6Lx16+BEBn87iaLdJFAa6emV3yqFCTH7c2vrTr9Z+9cxYAzp/wfQJg2T+7mg0r7gl+eX71pDGFjwrX2y91/tvluGIG+voHvwTQ89bv2wGs2fRGtT4fAJC/sPIN4TdrbUtgDNtV2fmTrpav3nteqbrlUVfgn3l54pWD7y72XL/A3Ox6SRw7MOOe+YX+Jw2If38+FfmBB4H8Qr3poHB9oOMpW/CY4Wfc/2ZEzBaXQ4z5VZmt4Rn1HKh/4E6ljxrbFy7fFflw/sK6toYSAJgC4BugYP2LdUJGYjZlBQD2tAfDjjEd2Yw0Bn/MG16LpYEUM4pI54c/EH/4u9qWtoZbgfB0evb/Zz8gjJBIVTrBBkoynYj+dkeJ2N+CDSSQ9jc/9jeljNjf2N9GZX9jA6V/Ax2of+DOta/5/xl4QwhktOflDqAkkA7E0XmTAGzbqxZ27OnkL7S4HCYAbmtjEh9niYhiNDQFvNOstQZqd53OZHM4bBEDtLXLgoO27cbkSvgXHrpH4Q7xgnseeiGyfFcz9a7HXnhS6XAACu55Ms4jxmfuvBkaAEe6toY83PXsrnMANDdPjrL/jT0XPwFg+ufnCh/e6IncPqbw77MnArCbn/5QfOjrzy8BmDg5S/aANxSNBYBPZG6GjxLzxb5vLnv37TgGYPUSfcimhVXrywB4vN0AkJMz6/qc67IxODjY9dXFkCceWje7/NGObwDc93TIwDwAwHeXB4GHXR88VQpgpz1wEcB7Qu27o/lLVgDAn073yG8/89auDpmYDULMnW+9KrdVktGhdbPLl+9qR8nq3a2RMT+3qvGPAFAQuTVfqwWAn64sBaR3AAifn7TFMxJKRzkjMeZ+9XQAtYxU0gGQry32/xhMp7tL+JLn2hSnY0hBOur9TWyglSvD+ttA+BPjyoj9jf2N/S1N+xsbCOnfQMrJihmt2dQ30GIK/3rl+4rxRqHUQDU2HYDOk7wGT0RDbigKeKfZaBd/1Nk8Pper2WIwzCgOf5rG0OzyOEziv5Ir4aeufKzphScfuqdAUncXFNzz0JMvND2WwKXyqXc91tQaebyCex568oXWpscUVpxPkeKbrgew390V9vixj/oBaMpvUx9Ff7nzXwAULCtc+2zXMfmnTMqeUzuuYsNVhZNCHv5i4AdeoLe/M/z5M68KDpWPL+bBi5c9J9wAVhkWhm2aprkdQEfrfnGM3MWLfV+d7/rq4mDk0acAQMn6xqWRm3KyrwNwsfDBTe+3yv2Gxh0zb5aJd2FdcGifjI88RwGsUIj5w/b3VLaKGZWuqdn+fkvjwsjbDF3/+SGAqav37o7cemjvLgClcx+STcfjehNAaWRGUdKJmhGA+6M2kEJGaukEMrrj8Wa51vk4/dIBVPubegOB/S2mjMD+JsX+JkjL/gawgfzStoFUNokZybYCTilvUqeUjmZGMQD3jn2s4IloqMnO9Z0Ub1O9WL7rbB6XRaP6ZI2h2WGyC/W+fY+z2WBI/MRT71r52F0rH4vx2Xc91tqq/tz4jpcyk2cqLXd3ut8LrfrrCfRf7gKAj/rGXHWD8rOunhTyzc0NPy3Mb+vu+Rej9kzRjyd5rykac/UkpV3ji/mS508R3weICmeWAB3Cz4OD78kU7gCA+daa6VsbP4dWE/7ZKCcnJ//abABffvPtYH6h9Jf3afy1QPj1/MdddTUwPbpInx/x0UpR9+kPVGNWGokQyGj+88fnKx/8KwAoN9wdtuHMoXV1lVsBrPhl9bT80G0/FP76EMCka/FRT/e0ONKBakZTpgLyKy5IG0gxI8V0IM3ov/9/ITMIzbcuum7r/i+BtEtHrb+pNNDZwE/sb8JJ2d/Y30KNzv7GBgKQ5g2kuCkko9BNZ/8i/hC5KQbK6WiLdEAsd44SESUp5QW8d98O8e3LVBelehcYlplgtwPCwCNDLLtktMkzpgI4d/K0wvapk4oBhUvrAD73fQFgetaEWM+35omaLXcB+KlZb7S73SfeBbAXACZOH3ttSVZeUfYNUYt5lZg9XW4AP56p9Ou+vesjQPU36J+72wGUFRYC/vGJkrnuLvd9db5PUvxLJrkpmjvjxLGTRzu2HV27rRYASstKbltxv2FRZeSdikpnlPWN6tYoGckdvG357Dzhp7Ka7QcrQz+OhE3b03+wdu3BuNJROKno7CAATMlNYTohMYdnlN7pQLa/xZjOpKno72V/UzipiP1N+eDsb2ygKOkAYANFF2MDlTw+UKf4TYeKaA3kPuEBrviPskQ0tFI+hN7jv/lYV6SNbQ/DMnEYvfuEzB3bNLQmz8Q5b+85LwzNLp/H8y/mH/t/73xx+lLP3ottDYOtm7/r7lKbwW64ZX0PlwcvXh4EgDF5116dnxPoxt2nUVZSWvYjADhx7GTZ/Rs316z2X41rP9qxrbax8s7ZeQuqJMvDpoFbS0rLSgDgaGPlgvpD0ul8u0+jrGCK5JkLZok/pm86nwitIJeR2ED+W1MyKp3+Xva3EcH+BiCdM7pC+xsbaITE2kAdG8KSJSIaNYZuGbniGfwGchQ4W/vsS3dUv3TH0safbGyDZtX/cnucz23Iubd2XPmS7PzpYwHg9KXOl789fDhtanhf31fnu/rPd336dddX3w5izHXXXp0vXpAvrGw82PLGwd0XfB6HbS6OvvnYo92Gg8KCN63bG2pWi7/UO55aXvHAlnT5zFH+m5Y3Drb0DbRubyjB0V2Vd9YHpt5FYWXjwV8tAoD7H28owYcdB99bsT2908EPhFaQy0hsoF+tBoBJmZYO+9uIYH9L74yu3P7GBhoRcTRQaLJERKPGUK4DT3F67W4AZ0/2Arh+xnSFJ8lMMic1JWsigNO+r+M++7F3Dj+86xyARavmzb160pgb5mWXPHpVxYZx5eVjAXyx99JnSruePfm/zVlZkyxNjw+2in+ysrLqDwHQztQBeLdL6Wvu6FPI3FJYCuBot9wRBge/7fnmMoDrrhkXtgKdxmBp/k0ZgF3PbTkDIL9w2vzqykbhl/qaEoQuUSN3xsby3Nl5wT9ZWVn17wIArlGMJ4aM1NKZNr+6fr0k5lATZkm2xpEOgFsKteEZ+Rtocg4AfD4wBOlEzah0dcrSmZ2Xa3Ymn06UjGJNB+xv4RmxvyWQEfvbSPe3pDJiAwVPOoQNpCaOBopDlHeh2IefphVvkz4rLsnMO52CMIfz9CNyUkrYldJeKS/gtUXi2nD2PTG+cs49/knvRuW7Xqqd7fpYYcv0STGMaZg0Rlgr6et+tWd9tnuw9fHvOoSL6l3ftT4+2Lr54nkcO3zSC/E2+4AxNywfWzwdwKW+8EnmgzGfVdgyVnt7xPIDou6uDrUQAwqn3wYElpOJMPi3i4MADjx6W15uVY3wm/jAE+OzsrL0+4tWlEC8iU5q2vznxV/qzgMqZ5TxSVcHAKXZ+GPKKEo60+4TYn65QpLOq9sAwPNpYGtIRtHSAVA4XelDUPfn8hMIITXpIJhRrcWfTn2eeBfihB+lWzpIpoE+uZn9zX9S9jf2Nzmjrb+BDZT2DRSFbMwC2TeEGCg3kEd1/VoiotRJeQGvCawXF1sFH5y0noPuf/YOAHR+fA7AIt3MsK1zb54EwNv2gfIMdgAwRrNkLHCp89+UR7xP/q+3tAG49IMpY4DgRfs+1ZpfVee03/h8Pl9ba07FBuGPz+erm4+c7DHCNzo7In61n/H+CUBJxaKo89PoH20oATqe2vzJdVfJbf/zJn1WlvGfAXRAOw0AbtGUA3C/djjBjG7WlgHA6t3H+wYCf3w+X9WEPwG4tXQW5D6sxJiRP507Z+fl1r+n9KwPAXT8RUznB1MAoPcPid+td/NPwzMSGkiIGcCbyabzj888kDu7cqvcU/qFj2in/OkUliachygyneN9A82GFKQDaQMppNMPiA2E0AY6wf7mz4j9jf0tI/ob2EBp30AAgLtOGlcAACAASURBVO7tym8I/QCw9dXUDZUXGiiyCbwnOwHoVi2+wj/KEtEwSP0QekONzX8N3mh2RlkO09tUZfV/Y2lalsQachlEvAx+18w1IQ/PfGLF9QC8Hyld7Pa7uihrIoA2pbvWL39m/1+/A6Cz/WoZAGDSmKnTAVzq/D9z5pVrABzpCv012HWp8zSAsROmhB8rSszZedeM0SxeNRfAtr2ukE0HWp46CsiszSMjf9H9pQDsxl/K3D0w8erbrhcGHeDWmkeFdVkLF6zQATj2Pzd0AFi9RB++k3j2Eu0tsicUv7MPj9kp7FV834NyW2POSEwHAHb9Z/jlB9fm2uBlh1zhr8Li6QDQv+0Xz/y2Vi6jKOkoZyTuOCkF6ezcqbBuwutPbugPTWdehTir0x92pDYdZwrSQUgDRXJt3uBvs2B/ExvoNfY39YzY31Qyksf+NsL9DWygtG8gQOWqeKCBXBH1dt8bsulEp9RAjVY3Rv+lKJPDF5PmEfnErrG4RvL0ROljCO6B11jqxGnlYTdq9UpFvNfZZNZrA+W7zlbD/46CUx84egFot2xZtkaYLLVgZsOWJYsA9LY9+07U/Sdlz1ki3LX+7fP/l9npDb7+57sudmz+tq0NwGxbi2XFkj8+MXMuMKbw77MnAjjtWqnXm3c89UJgqHz/5c8Of3f45UsAUD62UHk9uciYgbxJOdcByL/v3lIAW9c+sM7VAwDoObD9geW7AJQ2VMW0iEth5aaGEgBdbgD47FP/zfhjJp799z0W/c/FoR4fvrlZnAt3+tr1phuFB3Onfo0zPYFDdZ85tKVeODvWmEwKHw7EjzghMTfpjWLMD8psjScjfzoADr4lebzb9Q+lgRVurgGwZ3lVzYEzwGTxo8nRnXsA3Prfli6MLx3ZjLxOf8yP/ywl6Xzuf6DtD+IthT2vPDPvh08fBIBxknSmmV5cOQUATr2bunSCDZRkOghpIACHfn/UH560gaT9bXLwsyP7m0JG7G9RMxKwv6VXfwMbKO0bCPCPLAAkbwjodtUsWLsNwPSpALaJ6QS4DgIo83/tEg+FBrKDH2WJaLjE9lVbvBwmneIZdTpdxMZYv/LLDHPmzJkzZ07k44Hx5zmWdz0y+/U3WTYEn1Ox4ZE2n88XMnA9+OfeJWMnKjbB2PyH5z13MvTgHptyk+GO+8fdKzl4eTkATFwyTnpG+ZgvfdI/cLxN8uknqKymbUA6RHDTagDAf93r8/l8Fy5INx3vGzje1rBqhmKAMxfE+Tu4dM0m6dm3rwGA0oZWyenUYo4rI4QPhhSOcL/ylA8lq3e3bl+juDnSlPuipKMec1zpCC91WEZtDSuUL1JNWrB5lKUjNNBUxQDZ39jfUpkO+5s6NlBYzGygyIz6Blofv1UuuLIV299XSKdsxfb3ZU4hd/DIF1AuZp1N8jFI6ZNeegp8BLyyPo7HhK/N6HKltNcQzUJvaHYp1vButztkng+dyeHhYJhQpw7fYd37/JFzgQe8R9qqrS/Vnor5CFfPu2pe7bhfPGkK+bZk+tj8JePu3XBVycxj7+wZb927vzdwiusXP/duZdXD/qXjRHl3mGwOj+fFR8coTZyjHPPg4Lenz53v+w7Ir25p2x1cDhfQ/bxxU9vByvyY0wGQX/3fn14JADdKZwco/1FVw6a2gVd/t/t42+4VpYFT6HQm244/DLS27fYvdSOxYHPrG8/r1c8eGbPJFow5YmvJ6ob4Msqv/u3h91tNYR84phf8rGFT20BL48Jp858PzQiTFjQ8vTM8HXGF3kX/ECUd9ZgjNkH7cLzp1L3xfuvjZaGP5l6jXVK9c+D3v3soMh0Ac55MZTrBBko+HQD51b9tXAkA2dKuLzZQRH8DAP1G9je1jNjf1I/P/pa+/Q1soLRvIGDaLB0ATAkkVSacom5+oewbgn7jwbr5MQ7RjyDXQA6PyzK6h88ny+t1mvWB2ez15iZhBK7TLD7QJBmQG5gqPOTRaNvDJxiPPuG40nm8XmeTWa+Xzr2v1wdCjjtpveQo5ibp0FfF4IUAwvaLeqJ4nx9LYDGmKRNu2NESb454ohX7k77JC3idTYGd9IpDviPziKHdI5pL2rf15sRfylTJ8vl8Q3d0r9NcZbQrT8upM9nqaiyGK+0dr7S0FEB7e3vY4+OXNo5EOFHMfejn/36Ta/yzkinoP9s92NaGiUvGzZun9A3Qhddr3vs0/rXsUmTitROm5+DLr77uGQzfdMa+oMLz6+ONoRdMDq2bXbkVpQ2tb1QrTZkz68YJI5WRqyZ37Tas2D5QJzeeUCaj9E4Hqhkllg7StYHY3wTsbynE/pbO6YANlN7pIIEGisGsGyeEPaL0SS89eZvEe1pNjgTvL3ea9TKf93U2j2tGY5Z4f4HkK47ACUMelQlIfi9/mNHilt0eeFBeeETK51A5kM7kaGkOLW8kx/EU1SvsKJ+Ht8lcZZWrpnQmh0v2+fEEFo1KLRd2tISaI95oneYsox3Q2Tx1J7RGu/TZNo/LolEMIr52lx6m5qTcngpdd7gM7TrwGkOzy+fzeTwOh8NmCrI5HA6Pz+dzNV951fuoU3zT9d6Po82cN3p85DmqMtfOaJRhGTGdNJdhGTGdNJdhGWVYOsi4jDIsnWHjbZKr3gG4rVVNJ4fwxJrFq4RhpvILX3n37RCiCk6THaWKgxBzDFdX1Q/kthu1Steh7UblHe3GyCEJTrNWtnoXThPx/MQDk+U0a5WvxIYdLf7mSCLaHVUh1TtgqlMpp5No95OS+dZDd9AqjB8ZFkNbwIs0GoPBYGkOshgMLNxHh4J5v7zrnONwphTwPVvs28ruvy/RUXNpKMMyYjppLsMyYjppLsMyyrB0kHEZZVg6CbEbs6ILH44uKXB0Joc4EYDH4zDpALfVapc5T6qolowyBaO4WIAYqWTOAjFcAIB7x75odZnTrA0eyObwyB3GblSrlKXnl+wUcXKnOVCnBl9cyR5ua6Mz5OnJBhaWp+TsNsnpbbJHi7s5Eo9WuB1bJ9lJbehIEu1ut1rdCq99+Is/rIalgKdRa+68Gd5dr8vfe//F3m9bHx9s3Xzx/HBHpeiqcTNvnDDrxgnTc+S3n3lrl3b9i4p31rXXVuTlzs5bsL1niOKLU8+Wqrzc2Xm5komOw6lllG7pIHpGV1A6SL+M2N+kMiwdpF9G7G9h0i0jNhDFJlCXQWfzuAIDnjUaQ7NLdX7klFApGQNFm6Rg3COWoyaHq9mgkVxK1GgMzS3+aN0nPOpnDRxHZ/OEDCbWaAzNLp9DXIzLXq9whdbk8EnPH/JShZ489EwG6R5yZ0k2sFDepnr5w2kMFknEkqMl2ByJRauzeVyxDeROst0jX3u53IcZC3hSc+yVl372SqZcfgemmQ7WqSx1MwplWEZMJ81lWEZMJ81lWEYZlg4yLqMMS2e4BOp3na0lYgizZGHpoRIoGTtPhlZSwaItOGDb0Kx2tVYzozi2c/oPLZeycBqx9pS/piu72mAgj5BEgklEjg831IhVZLDuTDKwMMGvZmRHpwcbV3K0eJoj2Zdx1eJYB3Mn1e7qHTumV3IoZCexr9Osr+8EgOK64BwKgQcTID0OpbEbludULB/pIOR8923Xp98msuP854/3PZ/qaJKWX93SV53IjumZDhLNKMPSQbpmlGHpgP3NLz0zyrB0kHEZZVg6SCKjK0n8k9h5TohFXvEM2SKsxqazq998nCTN4lU6q9sN9459Xkuw0gpWhzLVcgiv1+vx7NuzZ4ddZdZtqcChlStIbZEOcIu1ddiT4ig8vSfFikryJUSQxuLyWVIYWLhA08qeHZLGlRwt9uZINlr5Dhe7GNtdITzDMhPsdsT2Sg6FZAp4/x0IKJZ7MAExfvNFREREREQjKlBi6oq0sk/QzCgGhrKAly8ZVatDr9e5r7F+R2fiBQsAwG3VZlmjPKfzpBeJz/rlL6GVXtwhDSxq08ofLe7mSDDaOF8TIMF2V/qewP/lwkjhEHoiIiIiIhqF/MO2JaOZlQtGb5M+S6s1Wu0JV++BwjbdDFVgype6ZQefx9gcw/wyJt/uSsJvFhgmyVyB19Y4HMsAQKuVeTCRA8b9ZQoREREREQ2/ob/AHksQ4Rd9Fet3ybTnAp1Oh+Li4qKiZTMWGyAuWR+jEV4IXFmKA1O+WC9fhMfeHEMRrayUtnuYZIfyJyiZAl6jCZnJT+VBIiIiIiLKREq3Ag/LhdZAySgEoVQwSmZVNzlamiOK0tiWBAt8ZzH0dz/Hc8d6ygOL4XAKEyDE1BzD+DIm1e5KX18Ech8hHEJPRERERETxMizzL/Yls/S3dCbzEFEm/lbYS5l/2LZ9j1P5gq90wvzIKk4yUXoU2iL/GmKyKadQ4GVSeHGb9FlZWVlZWeJK6SkOLOrhgsvAhd2PHlNzDNvLmFy7K6wtJ7fIwbBiAU9ERERERHELVvAyS2IHFv9W4rY2RlRv3qh7RZKUjP7SSnbtM0VepznWYdTBFd/sRrNT7gboQGWtT3KV8GCRq/bi+uvnFAcmOZzcaueS69oRQ+NjaY5hfBlVRG93u9Ec2UUDuY9U/c4CnoiIiIiIEhCo4N1Wrd7s9Iq1ltfZpFe+tThQmcJu1AfLN6/XadZrE7khOVAyGsW9IyurwPVst7VKWjF6nU1hJ40yL5lkeXu7UZpzIAN/ZR37inHRziS+uMGYzXp/xMHSOMWBhZ29KdhMzqbgsWQPFkNzDNvLmHS7242S5EMiG7n6PTXrwKcK14EnIiIiIhoBdmNWjMWzZMV4Q7PH1imUNG67URt6AJ1OJzfvt8ZSZ7KKtVPkTiaHA8Y4y3j/jdf+Q8hUVoG1u2VOGR9Ds8NkVwxfpLO1JD85m+RM8icKXeg+xYGFnN1qtMss92ZyyE5BF0NzDNvLmFS7Cx1YPnmTY+TK1pSsA58qXAeeiIiIiGj00FhcHui1kQPfTQ7Xsj1ZRrliwdDsMHUa7ZGbdDZPs8Fjjj8IacmoXDDKnhOAzmSrK9phtMY4r5qh2eeAXuFYSOXc6oZmjwNV8meSOUuKA1M7uzgnnPyOMTRH6qNVkEy7F9e56vbIDCXRmRwjetWZQ+iJiIiIiChRGovL53GYdP6R8dCZHB6faoVjaHZ5QnfRmWyOxAu24E3VagWjy+OwSc7pP6vP1WwxBMZ0xzSvmqHZ5RMOFnE0jy+VK6NpIl4pQKczOZTOktrAxLOHvGiB10x+eTlhvxiaI/XRKp8k8XY3NPs8NpO0mwq5pySyRGX5fL5E9/V6nR7ZqfkSpdVeGUvQlZaWAmhvbw97fPzSxpEIZ0hceL3mvU+/HukoUmnWjRMyKaMMSwcZlxHTSXMZlhHTSXMZllGGpQNg1o0Twh5R+qR35XGahcuXabtqOlEkb5M4okRyr0haSfk68ERERERERESUehxCT0RERERERDQKsIAnIiIiIiIiGgWSm4U+Ll7/+n4aDrwnIiIiIiIiis9QX4H3OpvMen1WVlZWltYvKysrK0uvNzc5vUN8diIiIiIiIqIMMXQFvNdp1mdlaY1Wu+xi8W633WrUZmXpzSzjiYiIiIgyjaHZ5/P5fCldV41oiGksLqHfpuUU9BiyAt7bpNca7XKFezi33ajVN7GGJyIiIiIiIlIzJPfAO83C2nkinclWt2yxVit5hmffvj07rP4K323V6sHlIYmIiIiIiIgUDcEVeKfZaPf/rLM5PD5Xs8Vg0IQwWCzNLp/HYdKJT3RbG52pD4WIiIiIiIgoQ6S+gHfu8ZfvOpvHZTGoXFbXGJpdDpP4D3s9B9ITERERERERKUh5Ae892Sn+ZKqLZUy8odlfwrt37GMFT0RERERERCQr5QW854R4Y7tpWYzz9hmW+Sv4E55UR0NERERERESUGYZ6HXgiIiIiIiIiSoGUF/DaInFeus6TMQ6IDwy61xVp1Z9JREREREREdKVKeQGvWbxKqOBjnVfeu2+HMOhet2ox15EjIiIiIiIikpX6IfQaS4tNKOHtRrMz2lV4b1OVuGR8bHPeEREREREREV2RhuIeeI3F5RFqeLtRq1cu4r1Os14rlO86m6c5xjnviIiIiIiIiK5A2ak+oLfJ3HgCQLFJ57a7AbfdqLUDOp2puDj4rM5Ou9st3W1HlX6H/AGL61ys7YmIiIiIiOhKl/IC3nPCbrdHPux2h1Xs4ZsVNxYrbSAiIiIiIiK6cnAZOSIiIiIiIqJRIOVX4LVFJpMplQfk2nJEREREREREqS/gNZbm5lQfk4iIiIiIUu+9T78e2QBm3ThhZAMgGl04hJ6IiIiIiIhoFGABT0RERERERDQKsIAnIiIiIiIiGgWSuQfeadbXdwKhS7UHHkwAl3wnIiIiIiIikpfcJHbi8u3Fcg8mgEu+ExEREREREcnjEHoiIiIiIiKiUSCZK/DaGodjGQBotTIPJnJALvlOREREREREJCuZAl6jMWg0MT1IREREREREREnhEHoiIiIiIiKiUSDlBby3ySxo8sa6h7PJrNfrs7L0Me9CREREREREdIVJbhZ6GZ4TdrsdAEzLmhHTWHoNTtiFietPeBDbLkRERERERERXGA6hJyIiIiKiDOF1NjWZ9fqsIL3e3OT0Rh3q6w2MCw7Zs8mZZoOEnWYhNg5evkIldQXe63V6PGGPnewUf+g86XTGcpCT9UZ7MkEQEREREdEVz+tsqqq3CiN7pdxuu9tot0JncrQ0G2TH+3qb9FprxI5wu+1ut91qVdmTaJglVcBrcLLKKNPVAQBuq9EY3+FMywzJRENERERERFckhRJcym03ajttHpclrBKPvqvSnkTDL7kh9BpLnSlFgbB+JyIiIiKi+IWU4DqdyebweHwBHo/DpNMJG93WqrCh596mKv+uOpPDI93R5/E4bKbAno0xDS8mGlrJ3gNvaHYkXcLrdDqTzeNrZv1ORERERERxCSvBXa5mi0EjuVau0RiaXS6PTajEw0p4774dwr4mh8fVbNBId4RGY7A0B/a01/O2cxp5yU9iZ2j2SQXqeZPDFyOXy9XM8ShERERERBQvZ6O/fDc5XMp3qmssLrFQce/YFyzEPSfE+n2Zyp7ioGP3ifDpv4iGHWehJyIiIiKiUcq5R5wQW2eriTKg11Bj0+lMJtuqGcHHtEW66OfwX7GMZ8SwMKd9+Fz4MvFLJpUP3Udpj/ATNekVp6UXD55l5uj/zJHyAl5b4xDUaFN9aCIiIiIioqBA/W6qiz6kV2NxuZqbLRbJxXbN4lU6ALAb9albMM7bpM/SGq126Yz4brfdatTqzYrD8PeZ9aH7+PeIUnxrZhQLT5cOLBDjEBcI41xjmSTL5/ONdAxXnNLSUgDt7e0jHQgRERERpdjo+qT33qdfj2wAs26ckMzugenrdIlPEu80ZwWXtdaZTKuWLVusDbmLPrGQoDM5WmqEA3m9zsYqo90NACaH9FK+9Ow6k62lxiLs4GzyL/cVlpq4g+RR/yFCDxyMJPxxGtWSWkaOUmv80saRDiFlLrxek0npIOMyuvB6zYj/wk6tWTdOyKSMmE6ay7CMmE6ay7CMMiwdJF1/UqoUz0h4Si1Ds89T5C+63Xa72263AgB0Ol3xqrqaxXEV8/5b8kPLbo3G0OwSz2I3mpfJVdQmhyv4qMZgcXmg11rdcO/Y57WofTlhWGaC3Q7Y9zibDcHj+ufn4/X3zJJMAe806+s7UxYJgOI6F78cIiIiIiKi4aSxuHyLvc59jfU7Qsawu91uo90qXEtXnh9PwttUbwcAna0lsuTWWFpsO7RWN+z1TTWGsO0yt/BrZhQDbnHyPLWTy1bwrN8zVHJX4N3S+zqSV5zKgxEREREREcVGozFYmg2WZgBer9Ozb8+eHZ3+at5tN2o7Yxmk75/UXmFAgEpJLreHtkgHxFJvyVTwrN8zFWehJyIiIiKiUck/gxs6T6ZwjXaNxmCwNDe7XD6fz+MILCCvjTqZu3/WOF2Rwnze/knvI8JV3CM2hmXCSnf2PWKIrN8zVjJX4LU1DseylEUCQMuJ64mIiIiIKFb+i9RRbxQXeJvM+7BscRy3tUtuRg+/yVyZ4h35/kvwKee/Bt950guDhvV7BkumgNdoEp+dkYiIiIiIKDmBijimCt7ZaLXbYbfuEIfDK83fHn4SS53JagyWx1EpPs9/iT71xApefBnEofys3zMQh9ATEREREdEoZagJDHFvjDLC3T/DHHSrFgvVtX9Ee2DkudKeMdbdgTXZT3jknxDlHvlkiKPo3Tv2eeHcYwdYv2cmFvBERERERDRaaRavEqpw2I16s1PpVniv0yyuFAdTnf9KfWBfu1F5z+DScNHr7ijfCIiFdbJ3vMsLVvCs3zMZC3giIiIiIhq1NJYW8SI83HajNktvbnJ6g9W41+tsMuv1WqNQO4eu16ax1Amzv0FmT/++WeKuMiu9RcYiHs9e3xTxhUBgBEDwG4SUClTwwlLfrN8zEwt4IiIiIiIaxTQWl8Ok8//LbbcatdosP63WaA0s7q4zOcLWgjM0O0wKe/r3FXeVW9o9kn9Iv9uq1ZsDXwd4vU6zXhwBEMP3AAnyV/BuN1i/Z6yUF/BOc1bCoi7MQEREREREFM7Q7PLYgkW8HJ3J5nHJzFVnaPZ5bKp7CrvGeNVcY3GJh3PbA18HaLVGuzvOAyXAv5ocWL9nLl6BJyIiIiKiUU9jaXb5PA6bzaTTSetxnU5nsjk8PlezYuWssbh8Po/DZop/V6XDeRw2k/RYOp3J4fHEeaC4BSp41u8ZK5ll5IiIiIiIiNKHxmCxGCyWBHdtNiS0p/LRmqM+z9Ds8yk9S2Nx+SICUttBnEXPzfo9g6W8gNfWOBzLoj/t5Mk9O3aIN6PoTI6W5lgWVCQiIiIiIiJ53n07eP97hkt5Aa/RGDSxzO5gMFgszV6nWWu0u+1GLRw+mRtSiIiIiIiIKBas368AI3sPvMbQLE7xYDfqI1daICIiIiIioui8zqYqq3soZ7mndDDik9gF1kp079jHCp6IiIiIiCgO3iZ9VlZWltZodWPIFpmndDHiBXxwrkRW8ERERERERHHRzCgWf9KZHB7el5zh0mkWevcJD8Dvi4iIiIiIiGKlPjE9ZZY0uALvPdk50iEQERERERERpbmRL+CdjVa38JOuSDuyoRARERERERGlq5Et4L1Os95oF/+hW7WY4+eJiIiIiIiIZKX8HninWV8fdUi82+2OeIz1OxERERHRsJp144SRDoGI4jAEk9jJVedR6WwtXO+AiIiIiIiISMnI3wMPnc7k8LhYvhMREREREREpS/kVeG2Nw7Es5idrtRoNK3ciIiIiopEwfmnjyAZw4fWakQ2AaHRJeQGv0RhYkhMRERERERGlWBoMoSciIiIiIiKiaFjAExEREREREY0CLOCJiIiIiIiIRoEhWEYuwOt1evbt2XMi5h2Kapo5GT0RERERERGRjCEq4L1N5iqrPd714E3LmsECnoiIiIiIiCjSUBTw3ia91hpv8U5EREREREREylJfwHubqkKrd51Oh+Li4hh2LdKmPBoiIiIiIiKijJDyAt67b0egfNfZPC7e1E5ERERERESUvJTPQu854a/fTQ5W70RERERERESpMXTLyJmWGYbs2ERERERERERXmJQX8NoiXaoPSUREREREJMdpzlKh1+vNTU7v0J9f3zSUJyHyS3kBr5khTlfXeZJ9mIiIiIiIRo7b7bZbjdoU1ddep1nPSp1GVOqH0BtqbDoAcFsbnSk/OBERERERUZzcVq05yeLEac7SGu1cLJtG1hDcA6+xtAglvN1oHtLhKkRERERERAB0No8vksfjsZnEO3zt9bx2TqNf6teBB6CxuDzQa61uu1Fr15lsdcsWa2NZ4l2j4az1RERERESUGhqNxtLsmoEsox1wn/AArDdodBuSAh6AZnGdaYfR7gbcdqvRbo1pJ5PD18yp64mIiIiIKHW0RTqAY98pIwzJMnLeJj1vECEiIiIiojTgOeEGlJa59jqbzHq9dN76iGnrvU36rKwsox0A4LZqFaedDz3WkE+AT1eiISjgvU1VVtbuREREREQ0srxer9OsN9oB6Gw1EfW7cN3RandLqhdh2nq9Od4b5k/sM4cdSzwS77ynVEp9Ae9sDJbvOpPN4Ykdx88TEREREVH8/BfGQ2m1WqPdDZ3J5nFZwm5/9zbptULhojM5PB7/rHcOkw6A2y6Ztl5jcfl8PocJQHC2vLDjue1Wu1D9iAdyCPN6w22tYglPqZPye+C9JzvFn3Qy/02IiIiIiIiGlbvzhMeL0Amz/ZcdQ4sWjcbQ7PIU6bVWN+xG87J4pugyOVzBZ2sM/nm94d6xz2thXUSpkfIr8OIdJoBu1WL2UiIiIiIiGmlue9hYdm9TvR0AdLaWyNI6uCx2HCvPyQzR18woFs5+whN/yESyhmQSOwBA8QzW70RERERENAzk14H3eTwO/0LwIWPZ/VcdFWqWBEpvuSNpi3Tx5EAUXcoL+EAv7TzJez2IiIiIiGjkaDQGS3OL/3b0HfvECsV/26+uSCu/n7+oibmmUTwSUUqlvIDXLF4V/v+DiIiIiIhohChXKIqDhv2X4InSTOqH0AduGXFbG7nuIRERERERjSzFclzxAntwZm6itDIU98AHZ30wavVmp5dVPBERERERjZSIcjzqPe5R7pEnGimpX0bO6/R4MKPOZqq32t1w241aYYJHXfQZHIrrXFwKnoiIiIiIUsi/YpxknSxtkQ5wA/Y9zmZDZAXi3GMXduCd7ZRmUl7AexqNRrvM4263O+q+vNGEiIiIiIhSxut1Nlb5yxPJOtcaS53JarQD9vqmGkPYSnL+NeZgquPy7ZRmhm4ZOSIiIiIiomHhtmqzZGi1Rrv/OmJoOW6o8U/cJb3r1+t1mvVa4YJ92Mru4sT04kR4Xt4nTCMi5VfgtUUmkynBfTlChYiIiIiIUk5nsrWE3ayrsbg8zZpAxwAAIABJREFU0Gutkrt+w54fevldM6MYcAtfFVgBmBw+3v5Lwy7lBbzG0tyc6mMSERERERHFTafTFa+qq1ls0MiNhddYXL7FzqbG+h32wP2+Op2prqVG7vmGZo8DVcEr+vACHGFPwyzlBTwREREREdEwMTT7fMlcQNQYLM0GS2xH0BiaXeEnUzu/xuLyWZKIjSgC74EnIiIiIiIiGgV4BT4tFcxseFC/7q7rhX95j7Q99+rhradi3Hlyw5Y166bG8MRzX3uvn6A27Kf33P4217OvdB2TPnj3sguPBSYrOPe89aXaUzIxA1hTgK1ym1TTmfna60sWHdk7/tku8YGCmQ0P/nTdXd+PIZ8YxJ5RRMwhx1HPKMrWa42HzLv/GEO04o1VTnOW7MoOwMTpY6cuGFs4M+R7uM92D7YFop2efe+j2VfLRAVg4lX44jsAQM+B7Zt/27jtqPB4yeoG06PV+nzJMa/Kzrt6XF6OeJ7BwW8/Pf+tf99XfrXk0UOfx5BOVKVlKyp+XWVaOE364KF1syu3+v9RVtN2sDJfPubfPvX4D3JjS0flCT0Htm9+onFbF1Ii9oxU0omaUZSt/7Tq7572XBzedCKi0plslQ//XC+3Kc4Girm/5U+5pufzb4YmnZT2t6cbt3XEkE8M0qK/pcUbQur6GxsoFelERJXSBgpuKtBOh+e0+Mu2dE3NL62V8wv9B9lSVV4bbEhtQ+vh6pDI4yLXQAaOoiai4ZLl8/lGOoYrTmlpKYD29vawx8cvbQQgFrHhO0lK5ShiLuBj1Nv2k+rDwYpXvoBXiTmOdNY8UbPlLiBYwMvum7ToGcmf93lrY7RkY9l624f+qU2jiFrACyYuGTdvXrCGly/gZaO63HfufN93B+rzlu8K3yT54IXs/Btzrotj3+SUNrS+IflcJf9xUPa8upq2Nyvzo6aj8oQhSAexZKSSjnrAiW1NTuINpJLsyDUQ+5uI/S3GfZPGBgrZOrQNVLL+/RZTIZDaAl6+gWwel3SyM6VPeunJ//lzxFx4vWZkAyAaXTiEPt1MbtiyZBGA3rZqa+P4pY3jrXuf7wVw/bqaeXNjOsLZ2uqt1Uf8/zqyd/zSvfsBwFO9tHH80kb/P4FeT7U18M9vAQDnnhdOurRx/NLGn2xs8wKYWv7vT8wMP0lv20+WNo5fKhSlMjEDQsz3vRRrOpPF6l3+pfDHfFGM/D+CMQ+e84cknCKYuxhkvBkpNQHW1cybG6WBYtmqsew+84nP5/P5fH0n9/f5fD6HCQCqHD6f7+N351ne9Qhbn5pYbQ2k499asSHH/2dc+ZKxAL7Y+21HxBXjiUvGVWzIqRCqd6UGGpP3/TM7Hli+C0Bpw6a2geN9A8fbdteUAjjauHbLGeE5k3KuA3Dx29Pnvn7v06/fOzfYdzFi3/a/XfD5fB6H7ceBCB7eeuGSz+fzffe3D/oGjgf/7K4EAN0MACiraQtuat3eUAKgvbai5kB4OqUNrX0Dx/uEj3Td2+Vjdjeu/cfNUdJR2veo8r5A6cP3A0BZQUTMx/sGNq0ORBm+KbaMVNLZckYt4IS3itH609m9Qgxpzaa2QDpTIjNKpoFsOvUXOd4G8sd8TSCCldXrywAAt5ZI0jnet1sfjDJt+1toOmJ/8zfBxjUAsHp3aH9bsynkv1Wa9Tch5mV1kv4mCXh7MKOk05F/kVPd3/wxT5E20JpNfQPH2xpKAKBsxfb3xQZa5g/S32Rp10B3LBH+76/YuFnSQIGYR/8bQmlDa9/7m9aXAeh46hfbewAA+dUtfQOt4rtEMhQbyFrVxAXFiGh4sIBPMwW3GacC8FRX+wddn+qqrd67H8DU8ifujuUIM1/bsia0Eg4x9yG9UMv9pHqPZBz76a1HAFxvnDc58NCxdw7fsdEDAHfNXBNnzAD2A5h6Z0Us6cjGLDls5zx/zCuEfbWaYMw514vpRAzLj3jFYspIoQnEA65UbaDYtm7tuSlPiNm0Z5nJ/90EPtx8BJha/vzaco1MRuJWaUZjbph3VXk5APScuKyUjkJGAL4E0PPW79sBrNn0hn9EYv7Cyjd2rwDQXttyCLgq+7psABdP9/vHzH93sa9/MGzfB24flwMgf8H9r/k/SMH3zVfnTw8C2ePyciJjcp8ESta/6L/GAgDT5le3CJ+St+11qaTTs/9NuZhNANo3vKaejsK+K6Ls+/JJADh6KiLmkJc5YlNMGamlU9vyqkrASWyVpDPvrd8KH0Zb33heMmz1gerVAI6++VZ3fOkoZGRxOaK9yHE00M2BmP8cSCe3yHRw02oAH3bIpIOiBalMJ7X9LTwdsb+JTbDzd1sBlGhvUYkxjoyGpb/hD1sBlPzov0j6W5DLGVNG6dPf9Pn+mP/fzZIGAgDX5toOoGT9i3WBodqBge4RYadJA1XP2iv836978CFJA5UVANi293X76H9DaK9tOVSoF98QJDH3bKl76qhKpDFRaSC3tdGZ7OGJiGLBAj69zJ03QwPgSNfWkIe7nt11DoDm5smyewXdveyCbcmiqfAe2fsToVINN3ll+fUA9u84fCx0w7FnG8cvbbzjlbMhj77TtR9RKMQMIeYJUdNRiFlyWGnMXcHDAsee3SpcG49MBxgAsEgXcaU9WkZKTSD8VVQ6U6WBikqLVLcK+/5l4CZpzF3P7hoAAHzvgNsDYNZU+YyEreEZ3VA0VjUd5YzQ981l774dxwCsXqIP2bKwan0ZAI+3O+d72TkABi9+EfKEi6H7jvleNgB8+c23g1hYtf5WAMLr/8XfLgK47nshc20I2aKsod5UiDDzl0R+3A9z5q1dHXIx19h0APpV01HaV3iCyr5i+qUyMZ/9QMynKjKdGDJST6fzrVdVAv7TW79T2aq8760IpoPDrUcBrPhl+GjSosaB430DLWFJJd5AhqgvcswN1C2JOaS/6RvfF64oRqYz8eepTSeF/S0snTIAfwUgNsH7KwGg7P775HpXpLTob92nPwjEHNJAABCyNdl0lDNKYX+TxvyQtIEA6GX/mySR0TA00C9CYg400LgfAsBA56h/QxD3RXjrHKgvr+1A2coF0SJOJB1/A53kNXgiGg4s4NNL8U3XA9jvDh8RfeyjfgCa8tuij6Lv9Ty/cesdz3ZFFLSCyTOmAvC0vhNbQAVTNAB6+zuVn6IeM4D/iJqOXMySw4bELOw7ISSdcydPR8b1XR+Am6aEv2LRMlJKR3Bz/nWyW4Wobs7/vupWYd+/hsV87CPho+3EGaf7vQDGK2QkbA3L6PznADBxikI2ahkNXrzsOeEGsMqwMGzTNM3tADpa9382dgyAL/8WPgda6L5jcrIBXB68JOx7MwDgo7MALl0eBJA9RnIN/g9CwbuwRu7+wx6PB0DpzJuV0/nIcxTAioiYNTOKhR/uV07nDwr7Ck9Q2fdLAIA+4kMtcOCAeIfj7fLfrkXLSD2dD9vfUwm4q71DZavyvuK48zm/rJ6GP3e3A1hzz3yF+OJMRyWjqC9yzA0UEnNIfxvGdFLX38LSuR1CZSIStt6O7i31D+TOzstduy2pjIalv4XEXPHUhwCAf9vbI5ORPx33Oz2JpKOSUer6W0jMLduOQmwghZgDZMNOiwYK2yrE8qELQOn3B0b9G4K475nQXVw1y3cBJetfvDsXyVBvIPeOfazgiWgYsIBPK5Nn3qSwRSjeonpnz/jqPbXvnFV8gqR8nXv3vNe21FwQ5zbLW3p3eAEy9+55f7SVawBv2wcKXweox3z+nNJO0nTkY5YcNizmysCEc3lL7758shfA9TOmR57jqjwAUycVx5eRcjqAF8BVqhnFtPVseMyn/ypez5k+yT8DjlxGwlZJRpc/O/zd7/deAsZOLVL+n6yc0SXPn5S+xiicWQIA/z979x4V1ZWgDf8RkIDBqKCRDCqXUEqMEk0QCDa20RgvEUyiX96Os9QX7cGR+KJ2f+iM8U13u4zdyIyiS+mJPcpSZ8x8tmiABEzbXhJaUggd4yUGqWrBCy2JghqMVBCo749zqupU1TmniuJWVXl+i7Viap/Lftjbsnadffbp11/pORVW+3YY2gD4BAiTAaJGhphLfH0CAPj5BJpeOJO1SQ8ASSm2n39w46S4yFBs6izltYWES2FyNE8qLt0oxmm8qbRv1DCVfSPFP8XPsPtQeybrDZW5o04kUokzNhbAQ8VauVx65tAXAIDwpGlWn79vnLQeUJ28arNjVxtI9ZfciQayqfNvhftvz/56bubBD7Q2cSaGig309y+7O0539TfbOOaJMuWnT14VS5Gfs3BdgdViWPn/vtuFRL3S3+TrfPuTxIFLdislunJYKO1cHNVE3dXfbBJZZonJ1BnAnb+Lf7D/vs9dGsi2dORgALgNYP4/jrkFT39DyAeAqnWpczMPmut8MnPlAfkpVJ2k1kBJXTs0EZHz+Bg5t6JyPRmAOHhTHks7wTREXLB+mfSJYsDAmWvSWtbI7KEvyLedV29Fpc4D/AGg9XtX4kgOq1bnec/VA8LE8tM2V5iFr9k1eYVZeZ1I5KgJ/Lpeeqf6JhBmX+fHZiSZv5uwL9W2/mJ0v1/IHNV33Lr+USEyBSKVRLrqcgAvxih9pKm6clNyaV15344f2gA/DH7MDwbztfr73wJDHrN5hzl+yvRxMHOw/HUQy6LB8oSLLfFRMpt8awCAYQMV43z5NZT2vaO87xXTdFn7a+zmOADyV4bm25YDcJBIJQ4A4PtuLz1+6kPhDwHtAK5WC8syl2VNNz8SCQBw5fDC5w7LHbULDaTySwbgdAMp1bkqP8c0ILSLg9otz03c0r1xuqm/qTZBzaIF4rrZcdk7d2ZMjsCZfx648kMAqH33uYnvdjZRr/Q3UyKxzlj1cuLeRgQAhgvv/tPBgc9KS7HLMqfgglyivu9vsE70L9i9QFjMfBhw+8K7/3TwFXG99DNZVvMjziwcOFHutH3fQBKSOgf85ODtDcgU6uwlbwgL8z/ZeH5f+t82LcwH4rN2ZowErivX1QmOGqj8sg7g4+SIqKdxAP+jFJaYGQZ9RfHSzdWVDh/VNuKZSbC/w7zXmev8wbC9uYnRANAK+IcK38MnpFxcj6WKNw5Y6+tE+eW6vASNXZ0HzhCX8WsGBppLHR/uweUOSB4j1xfu/tAWHuCHgIAYGOpMLwYMGhBuvXzdDWF5JHW6YzfQhcfzdrcb/3OiUalIiDNMuHalzJ0SyTdB1bqcKsQuOrIpZ8b1LEeTtN0kjrnOb3+Ulri3EZGvLhr2sfDxXRJnJI6vDFWbJeF+cXQbEiXjwwOHAesndTmcANzXiW7ovwIkda4TXg5/ddGVjw+czfnv+9JStaYR9HUc2CSqyzM91HNuxqL8vANnc3YdX5gzA7h6DfGxcWiuOuvgea9ukMjk6jXEx46vvXzxdhsMf1k4fdPaUYCnvyEIf4MWZCy6nnfg7IV3n1v5Kc6orj9K4FPciDwNp9D/SOkLbO451+ULjyurKLY8dG11/o6KpuiExM/ynHyCXc+yq/M1sc71zQCiE1I+K8xqKTQ/i66pAZA8PM+dEp3+cIqwjJ9Q59yYJwAAVwu0egBoeE9SaoqzZONh4fFyNSvMj5F7eZ1fRHh7XXFrWZnaKvS9wGCo/r4DQEBAQEyInx8A9BsUICxrZyYuNTQUANIP3bN7apT2fNai+AsH1qXOzbshd5K+cLXshNKECjFOpHizg9xzsNwukVhn+y9uYzee35czQ/IpPC1DeHyU5FFY7hbHus7+4Tkndk6XLQLEVeg9KA5M634Di46c+8h+gDQGMD+sy70SjUw/ca6h2b7O4bOFR38sLZIpTdsprGcuSeQmcaCcaKyQ6GvdDQCIWphzYt9HJ35pea4k5h908zeEqIU5J/b9IWUQAAwLxdmCLeJVds9/Qxg4NkdYhR5nTgCLjnRioUEiIrfnNgN4/fblk5f/6B/AYXd3tA3VxeQ6o6mkzHYOeeUHWj2snq9WWXtn3ebCHfXqT7C7U9MEIDgzN6ul0PIDAHjYCgD+j7sSx/5XoVznsIb3tmmP1YsvNrQonsKJROJ5beJYvpxug1oDOV1auX/PFEmdOwDgzn+UiXX+xLoUAHBlk6nUnMhnQIhf7M/8hgB3i9u/VTgp7tT81/J+/UJWbV9rKLL8LC8FoIlJAvB5te29jSZxY0ZIb263Yruv4cHD6vut9yyr3Q0JbHpY94MQvKMF5qfvzHhW4WwjI6IW5vwhK870ACF5T0dpAJzNSRw4MdTys+kkgCcDAOB2s2KcCc/EATh7VWaDofL71h375KLC0UwPE5r9jFJVnUn0dFScTJyJoQOXfwpAeKyxbIVdKhXrPCYQAAY9aSmQWRJ87NvZsbB6QFRXGmh5KRR/yWadayCbOg96Epg8V3jI87DRdnGGzO3OOD3Q35xqAmneSABV1dL5wG7X36wr/KRwY7N1nS3sSt2svzlVZ4lhAAq2Wo1s3buBIv8fyzPSPf8NIS5mFDB5XsrjADAmI8duzRUXiQ2k+GtMGqtRKCEi6kbdPoAv7fw4XF+6fHI/zerd5V07c33F4W2rVqWarVq17XBFveP9ZI91WHIgWdsqulZbeXeqbyqUWJY36xpx9bjGageT/CxVOqxtguzz2EwbVCstVRc+IFihxIk4kl+FTZ3l9q0/XfZ6hniZfaN4t2L/UAA3b9tNlVdPpNwEwo1tjxTKhFp1prTSXOdtf/eRbnbzdqWkNKPC+iC2iUJ8wsIBtDcoTba/U610v7+v5tlxCkXiXYXGR7bLz6vtazC01jU++LKy9hsAaH/4yLSIXVuHwfL0nQTFhYsAICo5NR5AQelxpQ3CldY71n2r+NddjBMyQmmwffW27L5inVWKFqWMVTqpSD1RVLhSleovXwAwQKFUSNTJUrHO0x83P9HLtFjUs+ERCoeyq7DrDaTwS7ZU2MkGsqnz1RuWexxGDn8cAIKfjFA6k21t+76/2capVupyznGj/mYqvaF0E4oT3KC/ye/rUKT9NywA3LmBQl6ZHyu+5NFvCJYGunHmwvcAcCVP8t2BsIQEdOtSQwdO7PTcAeUGEp7LQkTUK3rgCnz57jn9nB3DC2P3OV0du6O+YtuqFe/tP1UrGZbW1p7a/96KVdsOuzCIv3nD2fFtN7t0U35sOWlUCBysBu+c2tt6ANCkTrUrEsaHnb/If+l8E2A19z5wXg5MdQbwE5fiWH4V1nUW9n3guM4DAehvqiy/p3ZemzhCIgDX6+5BuYGu132nWqq0r3BX693wJI1KnWeolqolGvlbo9Fo1BYFmObeB6RueX82Avx8hHVzD9l99hLu+YxNnfVkewfsHuQOqO8rfGQdNdS0iJ2hvQOmp+/Eap7qbP1tjPppPGA1k/NcQ/OGadDXiL3gE+U4z2vkP2uK97ja7SvUGYD9IoGmOE93LQ1GaWTinGtozhp0CcCYuAkqFY6Ji1Uptd9XqPMYf+GheKOGAojQaAAg/5T9BTThc7DqA6LkE8k10PuzFX/J6GwDWddZ2t/QahA+r//NPs6t7ozTnf3NNs5XMPe3q5UXACC/WOby5v1a1+L0eH/bf3CueZKCUGpqIKFHPZ6/UlJq4bb9DVeliZxuIBf1ZQNZ9vDoNwRx35EA7iitvO+6UQpVEt4Qkt58lSvYEVEv6KEp9Lvn9Fteqv7cM33p9uWTu2HsDqBi24r3TimMuGtP7V/R6avlFeWnulwp11SW1djMYwcAxKyfHwxAf73Tgzc71UUVgOwwUlgC3fYC79AFicFQHTcq1BlCnR/IFDkVR3JYaZ1jLIc11zks8bP1spfTZWbdO0yk1ATCfy5XVas00OWqy6ql4r4bFi9rKcxqEescs36+MIAf8noCgKazT8yTlJqFCqV2iRo76q8B8A1SfBS8UgOFPu4T/eqbk2A/R/f4vnfPAtBERxl+aDMACPAbYrWBn82+AUEDJgwPmjDIDzi+T3zs80D09x8eAKDj3g8dpgXb42e+oj6AF2/SVhkYjxQuE9nVOWd1OYQP1spxlPYVNrDb17LI/BO2tTDHcXhfpYNE6nHGvfKWSoWffeUfVUrt9hXqPOaxUmH2p9DrZry0CJD7PHq5NB8AntFY3/frcgOVKvyS0fkGktZZ0t+AM6YU9kOCu3/t3jjd2N9s4pyFqb9dLj2sFAe4AvnHd/V5f6v6/hlzHEgaCKe35gOItCo1axZK7RK5Q38TL7raNVDz6a0qDXS2FsCilMm2r7ttAw28vGvdBQAaaamF57whiPsCuC6sL2r93cHO1wAAmmy5tRgcc9BAozl+J6Le0O0DePOjMHfP0SiN4cWxu3TSfFJ6bonu/dmunLFi23um4XbkS+/8vkjw+9+/85Lp8c2n3uvcEL7+Rp3wh5feKVKyJsGVujpW+3VJPQBNXt5raUL9I2Oy81JmAajXbj7dDWfIF+91T7m4Pjkt0vxyeFoCgKYdH0hmYkfGZOelZYYB0G1VeZKcXJ0BzAJQf77I5TiSw+JTsc71/yPsq9Nb6mxoApCQctT0HPsw89XSijPrbL7XcSaRQhNAqPNh1QZyrvSdn6BBqPOCeLEUAJ78BwAVZ5Z9LIy3LYmkpVaJHla3lWW33QWQ6KvyJDmFBhoMIOKVl+MA5K+cm3mmDgBQd/zg3DcKAMRlL5kGPGq71wbALzwkYIjwHPv+fqEhATb7nqhuMwC4eao09Q3TIue+I4L9AwAYWhsemR+krDozs+74wbnP5VQBSEtXWXAoYtZMuTrvBhC39nX1OAr7Fsju+5ePtMIZ45aOtq2qE3GcTKQWJ3vJWyoV7mSpWOcrNhOAJ4u3tr6xJCtPPAgAfJR3AEB81tuSe0e70EDbJ8+R/yW71ECYJ9Z57nhzf2u+nDV95QEA4WEycXD5RHfG6d7+ZhtH7G9CE5jjHBfn+lruf7Crtnv0t7wv4yOFOv9z9q655gY6fLgKQNovd5r723HJ7GWx1KrabtvfxAYS6mwKaxVHYN3fnEzUZw1k+iv/W89/Q4jLXjLt6hnxDcGuFbpIpYGScrNc+hRLRNRpxu6ny00yHz4pV2dTWJKeZF2DpPTcEp38kZxw84+ZKYLMP97sVKEy7VZhp61al6ul6oUXXnjhhRfsXxenN6/6XO7X0bh91RbpFOgVQuWs50Vb/WytMW1QVGo0SlcvD9ha05nf+HelW+0Oe/PzZOm5VOrcqTi6W1ah5Pe1Urp1ywptYyfSGI1GY6ODRArnFeusnsiV0pJ0AEgvMdXBOpFQqiLGN1ESJzERAIak+DtuoPZbjc3ntNmxMseMz9KaLlk0/tBirkR6iRP7Cl/ipZcYjcZHP3wtHMSyxPSRhQ7ijLFautlubepzDUrnTcrSOhFHZQP5ovCfa4/MB6zWmZfUaucimX1sDu4gkUoc9Qq7WDom1i6O3GZKXG4glV9yVxrIyvyDvRanh/qbbZwiy9JiTnK3/ma7WScTuXN/i8/SNhcdTHOwld1ebttA8w+e98I3BOubEayuwEtft1tmX/FHoYFsPu4qfdIjIuq6nphCH73qjHkMX75aM3m7eB3e/ob3pPTcEt2Z91fNdnnSUf3nZcIVycjFaxfYLY4VtmDtYuGKY23Z507fCm+6AB85coSr1eqS2rLxq4t3VFiWhtNXaDNW77G9mNwVpz8UTqF+lwOE5dwvnn79tKMD2tUZgFhn+6ImnbNx7PZtajI0WIqb81fnvH4a+Zv3TNmm05sbuAVqmnQZq/c4SCTXBADEOqs3UCdLJa6szRCfTm+bSMn4pKT03JLDW/o/6WhLufNea3rY8AiIyNinPZK1yPKROmlZzk7tCcsjcx+1VjcZ7rZbdjQYWpX2Tfx5bonu38YBgPHB/YfVjdLHyDki3ATw2m83THO0pd15Yxdll+jOLIyQKYJmqVWciIx92iMLJCsFxy7KFjew3xfA+IzUCLhOs3Sn9oSDRCpxFEotiTpZimEpv9b+1nad5Gk7hM0cfBruQgMlpecq/pK70EBhmnDzLKLIuLSd2uYN03ojTg/1N2kcYEyGtnnDNIxMP3FOe2R+nHODXnfrbxav/LpB3MzZRO7X30KGSR8xIiYaOW2HVzRQfIb2xIZpUYCXvCHEmt8Qup1CA63i9Hki6i39jEZjzxxZX7pcM2e38Oek3JI3D81ZLb3dPSk9d0NWF0buooptqe+dAhC5+Pfb7QfwAOoPr1qxv1ZtC8VjvvROD02Uj4uLA1BVVWXzunmlNPcxafGyz0acCdwsmVc/9bWWNRrUa6eYRpuyWgqz+ipO2vqsvAQc25ZjP0qXiQN3T6QSB7KJvj1i0GoxJMU/OVnl+7mWwqwvv3nQ3ZV1ypBBQeEBuHf/QZ3d4P7G7umpun85J33kz8nMiQvzEZdd5OhmxQnDg/ok0ZmsgSsPYP5BmU+KMnHgbCI3jIMuNFBfxUFnG4j9rXf92Psb3D0RG8hJE4YH2byi9EmPiKjrbBeX7j7Rs983lmDynN3lQPnqOZLBezeN3QHJanMRIxXG5mEjI4BaALU3bgLODOBNx3wpKQH1Fdu2HDSvbR8Z+VLywjcXqD8Gy7uMGxHswrLnbsvL4sDrEl3Xne36uu7uw8viwOsSMY6b87JEXhYHXpfIy+IQkRfruQE8AMx+/4xu7GSN5dJ7N47drajMdh8xMlIYwTvJPIEe5atSrde2r609VfveqbKXFq9d49y1fE8XmfyLhKaSD7xlfOhlceB1ierydh+In6l1uK67h/CyOPC6RIzj5rwskZfFgdcl8rI4ROTVenYAD+GGeFjG8GNf7c7Ru3m5eMUL8JJL8HU36uHExXPTI+BrTyk8Sq721P4VdXA4H1+YPaXizh2rcdfQoUOVtuwrk5JH6wsK5W9WD0v8rDARaNrRvTfnd0Vk8sXcRJXepRYH7pfIURyoJ7pb3FpUDIT7vfy234AeqWCn9fePEdalV3DjTwWajX+w3PooVbUuNXQdEJ84PBYiAAAgAElEQVQlvTeyb9XlLUlcd0G5XC0O3C+Rozj4UTWQl8WB+yVif7PhbonYQJ1n86GOiKjn9PgAHlZj+PLVmsmXS8649ry4XmH+TgBA5EuLJTPm6ysOHzq4X7gmX7t/y+EXnbyl3nNV7t/zel/XoRt5WRx4XaKR6Sc29HUdupGXxYHXJWIcN+dlibwsDrwukZfFISLv1pVF7EqXT950yemty8slq88nJcltMm5DJ4f25hXq1Nabc7jOnezWCpubz+hwjTuHV+CPHTsm/d+hQ4e64SJ2LuvDRex6iJcl6sNF7HpIH66K1BMYx815WSLGcXNelsjL4gCYMDzI5gr8rFmzwEXsiKhndO0KvHRQ3g37jetSZbpHwpqiojXKxWEL1i4uE4bwp8or1iQoj+BV3rWFsb0bzpknIiIios7ihzoi6jU98Rx47xb2YrL4nNG6G04/Wp6IiIiIiIioa7pyBV4zNj09vdtqAmCsppM7OLNAnTML3bl2UiIiIiIiIqJe05UBfPSq99/vtop0jcpD3k2ryhMRERERERF5MA+fQp+Q9JLwB8Xp7OYL8CqPiu+cbr+kT0RERERERORQbzxGrieNGBkJ1AK1ZZ/XL5BZY77+8zLhAnxk8otODLfNi8wrL1lfcUhchb7bvhGQFxmT/dbkzIRg4f/0FdqtH5TlOzeZYNLU5PVvjp4VJuzb3gpff7GkSV9RI3ecmKOFKbMqPp1S/phkR6C+SX9Tbvupr7WsMd/uIHlwunWdL65PtuzrKI51na3qaV30oKnJtzU4MFTcr1lf8ZXkLFbPTj+2LX8znnE9kXWdAaRFwrKjaiKVONJSvb7045xf7dtdec7UEL4RGt/oZJ8BMnGg3/3Yoo/ar1xov3tNfGVIuO+gWMv2Zt8eMWi1pv+RPAreus4xg/y/edh69xEAoO74wV2/yzlwViiKXZSd/nbG5AjLIQMC/EMf9xvsJ3zl12EwtJn3vXEyb9/WgoIqYd+Bj6P5e3GnpKRlry/8p3+2HEd8FHzp8n5zdqfv+WLGnfeyzTsCcfGxz8y3OS8AnMycuDDf9D+SBwtb1zkpPXfh0mWT5Yrs41jXOT52keW81kVjwjQGf901U9ONiV201HIo6wcLzz94fgmOSfbtZCKVOI4SqcSxK5U2kFocTN9VtOyB63HU69xtDSSNA2iW7jywXfJLs0o0c2120En2N/Y3NpAXN5C56Pim0DcKFh05lzPDchTbBmreMA2us2ugDVmrZker70NE1E2MHu7mHzNTBJl/vNmpQgeH26qVK9ZuTVErd84LL7zwwgsv2L8ekLrF9FNUKrNf4/ZVWyTbyP+s0DY6rIDu8F7rXYxGo9H4UHl7bZHVWbbWyNVKpc4O4ijUubF0q1NxLIlWfa6TvHhLeVcnEqnXWa1UJY6lVFeSLvssRQCA77h1dnFK0icqbx+x1CpOYqKkMNzvZZU6t99qbD7XcGS+zFHjs7TN5xqazzU017W0yyVqv3uveeeieIdvMrEbzwvHOdf4Q4vRWCKsnDFceftFR8TthZ+DaXK1UqmzgzgKdY6ff9CpOJZE2uxYyYuRymt4OJFIvc5qpSpxlEsdx8HgrsTpyway9DebRJ4ZB+xvbCDbOrOBoNpA8ZOnAwBsKmadSPiFuPojW+ekXMm/2Uqf9IiIus7jB/BGo2VMnZK5VXtTHKjfvKndmpnS+dG2ZQRvdTjbAzr7hYAsRwP4vduFg9/8fIUwXFxVZH4lWX0ALw5Em2+Jla7Zvv/0f4tVrVmxqmi7aWxpGsPvXSH91dysWbFKMrZftXfFYZvtJWexqoxtnY1Go+mVL/9bPY5Y58bSraZTmDcwfmcqOi0OPhv+flY6Or7wV+tE1nFcTyTTBKYDfp6s3kBqcWpWiKX/32zhn/shiX6v/fce81cnh3/tFxEulIhjeDGRLjdJ3N7/5XWWar+8zn+czfaSAfyQFH/VBrr1yGg0Go2PLv9rHAAgLnun8IFJeyTL9EpRQ/O5e8J27XfvfW8ah7eI+5b8HAAwRnwwQ1xa1rZdPxf2RXqJriR3mfhVgmlMdX7nIumnrLSd2vOST1fnd26Mt95e8nFQqIz4cz7Lrs7iryhu7c/V44gfLuPnHzQP80wbYIypaJf44Wz8K9Okn8Jf25C1SFpD6zgA4lxLpBInu0iu1JJILU7aTrFU2kC/njVMKY5dA7kYRyaRUVdieqXrDSTb3wAAmjFqcTTsb+xvbCCvbCD7IgB2A3jrOF0YwCs3kGQIzwE8EfUcD78HHgAS1rwj3giP2lPvrViRmpqampq6YsV7p0yz3F56Z43d89ortgnbpa46bHX3fNiCtYsj7Q9nfcDIxWvl59d3i8hn5oQB0GVkmCZd11avyyg+BiAscf1UlT2HZr+pAdDUHBQKoF47JePDdYfPLhP2hSY1vHrd5j1TCpoARM//SVpkzNG8tDzLr+ZWRsaH+bV3LMervZO/f0/gNp24fafqDJjq/FyqWhyxzvqCwtdPm05t3gADAegLymre/OksIdFvqxpbJOd9WC9JNOtzqzjCSV1KJNcEAMQ6L1BroL1LVOJofrFEA+g3xPyvUgBDUvwXry/euXBpoOm8zz3vF/u2MCZvv/Rph7mB9B8fKgcw0z/5DZ8BIZZ6DgjxiXpbGK63X/q0QymObCKgodFwD0Ddn/5cBSBt50emKYsRMxZ+dGQ+gKp1+076hD7uB8DwvaHOYDrDo7aGRsM96Ldv+k8AYSOv1AKIyy76aMfCxUtzPhcuse/+UDd71c5TRRvjAVx4N/dM3fFNc59becBcp9mH/rRjckSUpZIRUZPTTwgf1y68m3tGJU7dsU/s6rzqTMl8AFVbjqrFuXpwaz6A2I1/2DDNdGrzBrgCIHbj/43a+naBkOgP/zxkkOS8A59fmHPClChrpVUc4aQuJVKLs27fBzKl5kQbfqUSJ3/3r/IhbaCdKVf/+9fHbpvO2yyNY99Ar7gYRzZR9GxTnbvaQFb97cWn7krPm/hb5TiY/Bv2N4dx2N/YQB7XQJIi4KpiHWzfEFyn0EDpAMpX55R2xymIiFR5wQAeSFjz+8UvRcqXRb60+Pf2w3c1YQu2Kx9OOKDC7fHdY1Ly6GgAFdX5Vi9XbxaGqaOGKu86dHQYgNbggRCGapWmfYsqYN63cn/hjnoAmrzclFlh0FcUTylpBoCWB5dkj3q6+hgAhMQo/k4c1DlILU7U6DAATSVld2C9gVBn4F7Jw7GZwuDzUMhnuSmzwtDQZLVp5f7CHU0AnpogxNmmEwuaGl1LpBAHQp3HxsWohA0NVosTGgyUbnrvCoBE///77z+YmuA9PQAg8AkAPlE/8xsCQBsX+YxY+unlcgATR8pX98mxvgBwq+OhfLlyoraG7zv0Hx+qBLAoZbJV0YwlG+MB6PS1AX4AOu79YPP9QNt9g+5yOYDh9TcgfJQZGRAwIThgsN/sOcsA4FKNHsDI9D9kxQHIX5n4RkEVYhcdKVo7BgCGj5S/W3BaynwA+OpanWKcG38quCBT59lCnRvV4vzlahWA+JmvRMF6g5fEKzMTZg48kiN8OPtFdWriGwVVtlNhR6b/IUsD4E9nhDjm+aKamFEuJVKPc+lPH8iVionqdWpx6nUAhokNZIoTu+jIr4X5pfdvQa6Bfj0BADB4hGtxlBPN6I4GCrH0t5OZE+0aSLG/CW9E3RmH/c02jjf2NzaQ2zeQuUjuDcGmyKqBXKXUQFm5STD9w0dE1KO8YgAPhC1Ys/337yx+KVIyIouMfGnxO7/fvsaFwXbYgjXbfy9/PNcO2BnjRgQDOFZebfN65fVGANGJz0xS3LX69Xk5GRX+APQFf8lX3OzOupz8Kdt0qNft2JY/fnO1OM4PDBonP6Ctfn1eTuA800p1na8zgL8oxnlss+rB9QVHq0drLInqdTu25W/U2Wx1Z93H12zjAAgOcS2RepxREYNVSoPqtVOUj9xQkP9Yxj4AvuN+6mOKM35ztfUzFEL8Xljnv7XmzAKjVem5G5Afosf0T90SkGpaqa4ziQxtHcIo/M3ZM2yKRkY/C+BC0cdl3zz48puHDY/sjzr7faPxw6XfAIjduFr8KNPW1nD/4c1W6WZRC3eeLzqYhri0rIPn9+XMEL+H+OaGXv5ayYwNDc3nGkzrHsm5rjsLYL5CnQHMVIzzYInqwWM37gv/It+SKC4t6+D5XybabBW1MGOBbRwAuurrda4kUo9zpepLldLGuOwi5SOHbTy/c9Ftmzj7cmZYfwto20Bi6b2brsVxmAhdaKDIuEZI+5tMAyn0N+DBN24Xh/3NzfsbG8j9G8iqSL6BLEVWDeQqpTjRo8cBKD/0MUfwRNTTPH0VeomwhAVrEhascXLrhDVFRarbhnXueN1maIzS4vbXGvXQOFriVNxdf116BTgmNQFWl4Vr71TWfhh42lR+pQlzBgJP5eUu+0VFzdYPvraadt6lOj9sAoLli9TjmOuMmCxASHRaqHPM0TUAWgF/y+aHDwUetj+IxqVEjpqgv2qpfJk5zpWO+wCAoBBTHAAxqcJuTd+Jmw8I8TktaaCfZuUm7V5d/knrn6t9I2J9o8daTaTvSqJ23VfysxSAqJhY4IJCIeA3KACA7vxFANBERwGAwfClAcDxUwcAvPjmq9GAAQAiokZG7DhnXu93wnjgClD65vPTYxfNT397ltW0TMeuXvtaqc7DwgD5J0o6iCPUOX7mK7hWZEo0Tajz8U0LAQwDbls2fyv/3Fv2B8lfmfhV5xOpxImJBS4oTaxQS6QSB8DxUycAACOeEje3aSCxc/1pZaK7NVBs5A8Xas39TamB5PsbzqyZvuQLt4rD/gbAnfsbGwiAmzeQpCYqDTRNEqerlONoxiYB5d11HiIiZV5yBd6LCNPgm2quKZSHhYzr5O6TFk+eBaC+5rDiJfRWAGgBEBydkJiXm9ZSmNWSt+zi+uTsqSoz9p2p8wB/AGj9vpNxJHW2PbhY1PTAYbUamuBSIkdN4KdaKpdIEue28S6A8H5BdqWAwgV2ANGrzgir0F9rrytu/XO2oWitoWzXowtlbd82OooD1US66nIAL8YofR6rqr4u93JAkP9gAG1ff1UJID5KsvuN3b8rABC/4FXFr2cGAsIq9GcvHFi3MvG5iaEDJ86dviQr7+BJxRsYJf4mTKqMkqnzHQMADBvYyThinePmJ0fYHlws0ijfQGISqYFLiVTiAAC+Vy2VS6QSx1IKsR0UDYb7NVBc/4u2R3aigcw53S0O+5uEO/Y3NpCEmzaQQpETDeQqRw1Uftl2jiARUXfjAN7bTX3ts/nBQNOOnLJK9S0vFE9ZXbyjwnR/eVhwdEJi5pq0lsJlFxfHKM/b7wEqdTYXfex44Prlvhy3SKTeBGJpq32JtdnvHyryf3mpeZl63L3WXlfcps02FO16dLVabQW7bhcQEPO4D9DR8F17u03RyczUd88C8Vn/kelgtsi8ki++OGJe9BhVZy8cWJez8LmJodOX7D5+oyeqrcRc550ZtrMrzUUZzzo8zPO/aS7SukEilTiW0mH2JbZeOOIWcSBNNNf2PvbONNDMbe4Wh/1Nwh37GxtIwusaiIjIc3EA79WmvnZxjQZoOrZN7Q52s8ra6nWb9wTOywlcnT9lm3ZHRZMeAIKj56d8lpfcSyNelTpLi5ybEd/3idSbwFxaonRB34rPgBi/2LfFB8glpvhFhPsCwLX2S3tby8p6aQwfEBAzyA/ouHff7sb4k5lLFuYD8fMPnljoxBTLyKgZC3NOCI8jKjqYnbUoPhYAzl54943UuXm99IlQWucI5SLlp9ZLjYzo60QqcaSla+c6c7C+j4PubKCg4V4VB+7QQOxvqvo+ERuIiMgbcQDvRo5OBXCnph5A8OhwhY3qFVZWF1l2n7R4WcsaTTSadqze8/rpTlal9k7l6bJ1m/eMn5czZZtOD/Un2N2paQIQnJmb1VIo/pjiPGwFAP/HnYsjV2cx0fNvdSFO5xKJZ5TGERO9AABog5MNJBdnWL8hAK4ZH1iXXulknAEhPk8m+8W+3T91i39ioi+Au8Xt3yptfafmv5b36xeyavtaQ5H4069fv00nAWhikgB8Xq00ETLOaiHlgKABEwb5BaCjoelhnQHA01FxAM5evYobu6dPXJh/AfFZ2hMbOnu3YUTUyGkZC3NO7GtoLjqYFgvhAUJKWz8dpQFwNidx4MRQy8/yUgBDAwDgdrNzceTqLCa6sKsLcTqXSDyjTZx+/fpt+hwA8DiE37DDRCpxbBtoQs/FgWwDmfpbFxuI/a3ridjf5OOoJ2IDuRoHPdxAjou6m+V3KC9prP0q+ERE3YsDeHdzp/qmQkl4iKMV7Cy7T3hzuThte7X8hd+WwqyL64VL0DFH12gAQGEN88rTHy4taAIwKylG8aRNCiXhA+RXsIN9nKFp65fJ1VlM9JME5Ti2icQ7+aIV7nZ3lEi5CUJCogHIrMdun0gpTojPIAAY9uI0tQb69oihaO2jC8JF9epPNf369Zu8XeEivc+Tb/gKj45vsF1k3pJIacKCr+ZZpTUVrlZbLyDkM2TQAHHmfJP52ntU+DMA8MXWyeLERa38dZ6JoQOXZAmXaI5vWpgPAN/ZbQYAGDltx6aN8QAKSo8r1CwqXP5xRsDV2/LrIcnEuXoma7pcncVEnxxQjgObRGJ30ckvcu4wkXhGGbeqL0DxL6Z1IgdxOtVAHwgrWimsyNCnDdSF/qaA/c2C/Y0NJBPHzRtIvagnKDeQ8EwXIqKexwG8GxEu0l66KT+2nDQqBIBe+7X6rez5H5xvAkLDnkC9LkPheWZpSRoA0bhdCZiunwPBT7hc80vnmwCgojhwXo7wI8QR6gzgJw7iDM3OS8tLCJar89CYCOEPzfnKj2ezTvSD8GLoaGdW4JOLc9M2jpBoc0sIgOt19+CggVTiwCf6rUigYdvuUuUG6mjQAmjHMB8AGDZkIoDyQ6duuRYHwKWRvzUajUZtUUDqFuHHaDRumIYAPx/N2CQAh+w+e93QfwUgNnXWSADwCQ0ZEB7gg7a2a9aPlJv89towoLbqAuLSdio87OdMaT6AC9CMBExXY4DrrscZ9dN4AFh05FxDs/nn/dlinQF84iDO1YNzn1t54Kxcna9e+1L4w5gM5WcXWScaJLxY/4Uz6znJxdHIxDEajUuCvgIwJm4C5D4cSxKpxHGlgZ4aDABo7M4GEvpb1xvI9f7WBexv+LH2NzaQ+zeQStgeIjSQfRPoay4BSHpTef1WIqJuwgG826ksq9EDSIhJs3o5Zv38YNg+H87e0LS3njNd9JafbD9p6mt5CQCadnwgXLC9c1jXCgBhz2fLr9tqOvVNxVOr1/mBgzhD09anZYYB9dopGR9aXywbmrY+LXO4MGt9YJrCXet2icQF4YIS5rmWSCEOhL0uV1WrNhCU4wAYmrZp46okALvnjP/lHrmzd3x7pLUOQLhftPAdQUj4nCQA5e9ta5O/BlLdfukaAN8gxYWIFBL5hT7uE/3qm5MAHCg+Y1V0fN+7ZyE+r8tnyKABoX5AW2t1o+Gu1WZXz+zaYrqEEiN7leXGycyVBwDEZ70tPDU3KjlxGACU5/xe/vOteOpYzdNKcUa+Mj9Wps6lwo4hDuJcPZP1XE4VEJdd9NGOyTZxsp7LuShMUr2Sp3BXp10iccZH44F/OljnSiL1OONeeUuu1JwIynHgWgONexIAULPDtTjKiY53QwO53N8Ur4iyv1nFYX+zisMGcv8GUgzbU5QaKGd1OYBxozl+J6IexwG8+6n9uqQegCYv77U0YfwZGZOdlzILQL1282m1XSctnpeXAHxz8zqAsMTP8pYdnTrUMuiNjMlev+yzNRoA+oJC87XfynLhm/WBmbnLLq6PmWQZ9A6dNDX5qHBq6LbuV/7uwL7OgKnO54tU44h1rtdOybBdpN1S9P9q9bKJBoTJJjIJdjGRXBMAEOt8WK2B/jhKMY4pUfSqLaufAHC3uLVs16NvGy2Lz938ou3CrlatFoDvuJ/5maZKDvrf+3KTAFS3/XnXowvVHZZhfGPHt2WPyva2A0Cib5Tyw+HlGig0JGAwgIhXXo4DkL9ybuaZOgBA3fGDc98oABCXvWQaAoICwgOE0XurwfqoN3b/08oDwPiUGABV61LnTt908qrlM66+dPvKl1IX5gOI3fgH87WRkbN/CgCoXv/89CVZx2/UmXe4euNk3ibh1EhLT1deBy9i1ky7Om+fPKcAQNza11XjiHWOyy76yHYFY0vRiexY2UTNXxzMmm6fyORsTqJLidTiZC95S6bUnOgVvWIcOGwghThDxQNdcTGObCJ9qanOXW4gpTgAtP+q3N9wYQ37m8M47G92cdhA7t9AcmF7kEID7QaQlJs1uzerQkQ/VkbqdS+88MILL7xg/7p5enPAqs91Mvs1bl+1xbJN6pYVWqPRKJ0XXVTqXAV0kqnUAalbArbWGI1G40PlHR7+fcUqu+1vfp4sPYhKndXiOFvnTiQSqmc06hqVd7hZ4yCRehMoljodR5ebnqT4t9I3YqlVQ+uMRqMud6LyX+PxM/1flsRJTASAISn+jhuo/VZj8zltdqzMQeOztM3nGlrumjYtSQcApJfYvOBQrPVc0HMH0wDhOfAKhr2yU2u3fVx2kfQgKnVWi3NkvnN17kSig+K0hliVqdpxaQ4SqdVZpbQH4jQ075wOQHjsswL3biD5/qbCveOA/Y0NZFVnNpDTbN4KrBto/kHbop2LlPey+ZGvc1Ku5F9ZpU96RERdxyvwbqm2bLz0AeaAvkKbobR+m9nUmFkOjtusr9BmrM4Zv1lutTPTc+D1ktcaWoSiqnyHT6FTqbNdEZp0YpHjOqtTS6Q3PQdeb1vw6ZSMDx0ksq8zYGkCpbDhTseJXvX+GZ2uJDc9STqOj/WNSPF/eUv/WPvV9aJXmZ4D7yt5NXR8em6JTveHt32UVjZSTmQwtF4TlqOLyNgnfcAvkLQsZ6ewGlCAn+LHttIPdzs4ZUzSspyd2uZ9OTNkCk3PgZd+DIoUJjvP+ufJEY7i2Nc5PVess10RNEvFopPFBY4OrGpM7KJspUQa02OfbT/YTd9V9NEOB4lU4siVxi7K3qk9sfBqF+OMUIljfuxzdzWQWOeInmwgzVLFOKbnwLO/AexvbCBZ3tdAPUaugUp0Z1Zx+jwR9Yp+RqOxr+vwoxMXFwegqqrK5nVhpTR3M2nxss9GnAmUjpCnvtayRqM0S1zQUpjVh3HS1mflJeDYthz7B87JxIG7J1KJA9lE3x4xaLUYkuKfnKz0FV1LYdaX3zzogco6ZcigoPAA3Lv/oM5gW3Rj9/RU3b+ck36CPJk5cWG+w3mSE4YH9VWiM1kDVx7A/IPN9o8vkokDpxK5Zxy42kB9GAedbSD2t971Y+9vcPdEbCBnTBgeZPOK0ic9IqKu4xV4cmDciGCV5es8jpfFgdcluq47q7IYksfxsjjwukSM4+a8LJGXxYHXJfKyOETkrfz6ugLk3iKTf5HQVPKBt4wPvSwOvC5RXd7uA/EztcqLIXkWL4sDr0vEOG7OyxJ5WRx4XSIvi0NE3otX4EnNpOTRcqu7AwDCEj8rzGopXKbwqLa+EJl8sTCrpTArL0G+XC0O3C+RozhQT3S3uLVoraFol8LD5/pCf/+Y4UEThgeFB8iX3/hTgUZmMWcAQNW61NCBE0OnKzzKqC/U5S0JHTgxdODKA/LlanHgfokcxcGPqoG8LA7cLxH7mw13S8QGIiJyV7wCT2oq9+95va/r0I28LA68LtHI9BMb+roO3cjL4sDrEjGOm/OyRF4WB16XyMviEJEX4yJ2fcCzFrFzTd8uYtcTvCxR3y5i1xP6dlWkbsc4bs7LEjGOm/OyRF4WB1zEjoh6F6fQExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/Qz2g09nUdfnTi4uIAVFVV9XVFiIiIiKib8ZMeEfUcv76uAFkEzsvp6yp0m5bCLG+KA69L5GVx4HWJGMfNeVki74vz5TcP+roW3WnC8CA2kDubMDyor6tARD8inEJPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPIpaSmkAACAASURBVAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkADuCJiIiIiIiIPAAH8EREREREREQegAN4IiIiIiIiIg/AATwRERERERGRB+AAnoiIiIiIiMgDcABPRERERERE5AE4gCciIiIiIiLyABzAExEREREREXkAv76uAMmJjMl+a3JmQrDwf/oK7dYPyvJrndp10tRX9v6f56KdadgWIFB1g/qmY9ozm/dXV0pfnPpayxqN6X+adqzes65Wps4A0iKRL1dkH2fS1OT1bybOChOPeazA5qRDs/PSMsOcSOSQ84ns6mx1HGcbKOZoYcqsiuLAzdWSF/sijl2d0ZkGUk3kdJwLOn2sJrpn4lg1UJfidCaRQ+xvfdjfDEBAT8Xptv4WGZP91k8zE55wIo8Teqe/Ocjb2FaW3XbXidqmlxjfnw2ULu83Z7fsBkPCfcOm+0bFWF1n+PaIQWuubbjfy2/7DZCpFYAh/XH3EQCg7vjBXb/LOXBWeD12UXb62xmTI2RP6RcxPGCwwfDl/TbxhbrjB3cdDXt3/yq1dy4TMYoYTEZbx70fWhsetBmkLwYETBhk/ge7o6HpYcMjmTr/7t21ll3Um+BhdZv+RFvdNeH/fCNSfKOTfQaYd3a6gX72n8YPlnlCA1n2nfTzbb9Zt3p2NIAOg6Htm4fVu2Y+l1OlEjJtZ8OOySrlSuQaaLYznYSIqDvwCrwbijmamyL9dy46ITEvd1l2pOM9Jy1e9tka50bvcDR6BxAWPGt+ymd5yZMcH8u2zgBMdXYQZ9LiZZ+tMY/eAQgnfS3NuRCd42wimToDMNXZ2QZKW58yqztqrahXGkiqZxN1IY6kgdwmDtjf+rS/qY/e4Q79Tdi3m0bv6J3+5vo/T51291r7pb2tZWUdjjeV6W/hwQNC+wM4vinxDfNAC8CFA+tWJk4/WCdzlCGDAgZbvSDs+zcX6i7Pz2fw4wExIf6O+qZMnedoJrc9FP5PvQmqH/15r3n0DqC9rrj1z7tM+3bGE//gYAN3aSDLvpX/uWaOZvJ2PQCfgAD/8ODAIT3yIVe+gbbre+JcREQyOIB3N0Oz81JmAajXZqzOCZyXE7i6eEc9gODMLEcfyyKT984PBpqObcsPnCfue8xSrMsQX9TqAclmkm0qisUd5+UEzsuZsk2rBxCW+Nn6GNtz1WunzMsJnCdcnpKpMyDU+ZU96nGU6hymyZOctLrR9Cf9p3Z1/nRHvVgl4RQZF20q2dlESk2AzKzkSc420NC09Vl5CbLtdGddRuEnLab/u1BsFadeO2VevjRRRoV9JXuxgRwnurMuI99SyRZdhtjrbn1iSiHW+f9+OH5ezpSCB+KWTa61jnwcUwN1PQ4AVN80/amiOFBaZ/seVdBk2rTpPesid+pvkgaqKFaOI9kMtzJcjINe7W8Vn0qjyb6/fQnrDdylv0kOu0Wo8wO90JvqdX8xb3VRpzdVSXx/M+ftg/c3Z3pjiN/iL28ZTc4XBaQu9QWAJSVGo/Hm58mpe7ffNBqNRuO7QyTvb+mm0i0Bph//xBRfAHeLWy9U26TBkBT/1C0BqcLVXaX+5hP6xI1Dc98oABCXvVPbfK6h+Zz2SFYcgLM5K/NuWB3RZ8igoHCrgfXVg+K+8/waHza0AfrS7Un9+vXr129S8S3zVm1F/6uf8OLSXcH/fq6hua7lNxMBoHR5v4S1FQ++/MbyU32/1QDAzz9mkN237G2t1d88+PKbhw2PzOe1qXN521/LOhw0QWNb2d524ffz8paA1C0BLy/1GwLgWttfzcPsEL/kXy0vMTXQGXMDDQQAhPv9rsZoNBqNjaUxDYHzch6LFC6/p5cYjUZjzQp3bKDsnV88MhqNRt3Hm+MBlK9+a0txk6GhDYDmXyse1TU0n5P81LUYjbrcSQAQn6V14fK7YgOtXsIhPBH1Eg7g3UzkM3PCAOgyMkwz4mqr12UIY9rE9VPVdk17KzEa0BcUvn76jvhSbfXr4sdZ1c0AoKkBQEKM9Lp35emy8dt0sHvdmToDOAYg7LlU1ThqdRZOGhlzNC8tL9Z0rsbv7Oo8/k2bU5gvNdj9xpxKpNAE4gEXONFAQp0VBoeIjDm6e95M8/QH80get75UOoW0VJKoFxrIUmelRDZFF6pNdX5QKHOc5L3zg5Sq2ZU4pgbq7jjqdRa/flLkLv1NWhoYIx9H/SDOx1FIhB7qb7E/tdlM4f0NBrtq93F/kxwWPxXq/MfxaeLXl9GA+P42XhMNoF47JcNuWn7vv7851xs//+1zoebTWd7fruyqUHl/u1Zll8jnyeT+iYkAUHdZ7RqvQn+7B6DuT3+uApC28yPTlOyIGQs/OjIfQNW6fSdNR+jvFxEywHpwCNQd+8S8r8ZvsB8QPeO1o8K+H90AgPbvAf2u7EMA4rKLPtphmvUdaop/9pM/XZUe0mBorRbmfgf4DVGKIz2vpM7pAO4Wtw9WbYKky8a7ABL9k01z5gfE+CUvFYbZ7d8Kv66Yozt+/R/mKf6+5hM/6xcBIOzffqaBzN+ga1UAoEmdannJbRpo5fzxfgAMT02dXWTa95O2hkbDPQDwG2Q74UG/fcnqSiB24x8WRqjUW4FKA5Wvzint/AGJiDqPA3j3Mil5dDSAiup8q5erNxc0AYgeNVRl3/zNOYHzcsbvt/rYitrbemc2ww8Nsgc9XX1M9nXHdYZQ5yCZIkscB3We+lpLbsqsMOgriqcIH0xl6tw/FEB94yVH9XQykVITCP8ZGxfjoIHU6yyUDgcANDVZl7V+I1yyDgmJlklkKu1kHOVETjWQpc6yicxFetnuc6fGqs4xR3MTo/FAvqd1NY7YQN0Tx1IUJtRZ7rqKEKfpE/VrLu7Q36SlsRqZOJLN3utaHOVE3drfKoqnlDQDQCD0FV/8RbKJwvsbDJDTd/1NelhJnastvyXJe/KxQ2VWt7UDQDOAWUl2V9p7sr+NjRvrTG+cEAgAfymx7Y3Hy3UAZk17Vu4g9w/J/hv35FhfOKDU3xq+79B/fKgSwKIU66usM5ZsjAeg018FgICACcEBg/1gMBiqzXdWAzf+VHDBtG/AY34BAAxtd037tgCA8f73uo8PlQOY/4uMkbb1mrG+oXlfepTty4a2e6pxpOeVHi0rNwlAwk+j1JpgWEc7gIix1h/sYnzHhQPAg0axN4ZDX7p88m9sG8gndktA6r+MCQfQVFJm8zfofkUF4KYN5BPgB6Dj3g8d1vu23TcAQICf1e/j2s4lq8uB+OxN9q3jBPUGulTDa/BE1Bs4gHcv40YEAzhWbjshrfJ6I4DoxGecuF3T2tQY524iDZoAuWFw5DC5waQV9ToD+Etn4wh1Fk5ar9uxLX/85mq7z6+mOn9z/zqAsJBxMhs01Vyze81RIqU4glERg2VLrRKp1Rmo1+04cF4PILi/dYH/8DAATTWXGvUyiUylNol6p4FUEglFO+vkPrYMHS2ps3ADsL7gr2oD+C7EMeuGOOYizfNCne3TmeIUFrbYlXUmUW/0N+tS2TjmzWS/JnI+Dnqnv5mL9J+O31z/wH4zO4Nlq913/U39txQEiO9vgPz7GB41ABgxzPYttCf726iIJ1RKTb3xlnALwxeNsHWtUQ8geKDsQWpk/1F4eBsAhgxTSKOWyNDWobtcDuDN2TNsikZGPwvgQtExcZJ2W1vD/YfV963WlruuOwtg/uwZAAJ9fQDc+6HNvK8wzetR25XL5QB+PnOaSgWt9fcJANDWofS2IT2vVPTocQDGjXhSNqzYBC/NSAJ8Q22/1vEJegpAe/3lDgD157Yvn6yZc6FKfu2F6tctN484wS0aqK1OvPtAbl9bx9/PKgfwv7Psv3NxinoDlR/6mCN4IuoFXIXerQyNGaFQcq1RD9UVvOVMmvra3jUaAA1AqPJmQYBw/5vNdZ5JU5P3rkmMBvTar+XHBg7q/LAJkJ9hrBzHXOdjh8oqTyPwtKM6H/ygKCkrL0GTl5eMnLL8WsC83G59zWHrTyFOJFKOA+iB6P4KZeZEpz9UqjMkpdWjn8tLGAhIlxJ8aoJQ5zNl1RWJ5kS2pdar9/dGA6kkshSVbdUn5kUDsTFpkcIHtaB5eSkzzXWe+lpeAlCvXbr/ux3z5Q/W1ThA98UxCRbrvN6mzpY4d8ZtUqyP+/Q3ABgTCgAtOpk46gcxcaf+BlxpwJyB1vfUyDP/Devm97cu9Tdn3uoHAtA3ITo4eHQ4YDug6h8K8Ws+c+V7vL851RuFxwQEjw6xG8GHh0RDefFUu38UOr4ta9cWtwO+YWOVLzUoJ2rXfaX0NUZUTCxwQfizwfClzPSMq9e+Nv/Z5zHpB6WomFiYlrXTX7kA4MWYcODGybx9W9cVVMG0Cv330unpgoAA/4hB/gGA4Yc2mXPanteaZqwvksY+rVAq/PaUJrIHDfMF2gGc/jCwpOzRn8sxJEUsuv+RoWgvAKC2/dtGnydDFA4xKCEBNlfm3aaBlPYVJs8LV+ZFZ7LeOAggKTdzhtK0HHVqDZQElLtwSCKizuMA3q1YXbGUYf1xTYVkwaSmHdvOjF4jv5izzbpKs9Zktayx3UZfkG8/H9W5Og/wB4DW752LY13nPetOy+8k2az1k23bXz8NnM7H+nl5CYl5uYl5VsdP/Kww0f4IqokcNYGfaqnTDZS/OR///o950f6ITWkpFF9sqNC+ubmsEqjcbElkfXyZRL3WQOryi6/lrQlHoCYvV3hm1VMzw6Cv0C7dXFaJmKNrNEDTjpyySpiuDgW70DrqcdCNccTviPDAqs4iaRyYJkoEv1OY9Y7cgdyhvwExR+cMBACdwoV6W0/lFWblyRW4SX9zRg+/v3WlvykeNm1ZomkQ2/rJtu2FSVl5CZiVFIPTNhcwhSXHNPbN1IP9zaneeKf6JhCGWaNtl1qYkWR5mp3dQbSt21/UbJc5qu+4df2jlEaVUE2kqy4H8GKM0kTpqurrgMJl2L9drQIQHxUFmGZoG9rtt6rWAujX70zW9I2SNckBAIPChyNc7tCG7x9WP1AaakvPa2/c6Keh1gRPjx6H8LNKy4zcvQ0AD24LOTr+fh3QwBLqm3ZtNhR/25+826/fu3IFbtBA8vs2BPkPBtDWdu+R+dXjpw4AQPqGVZF4UKdcaWWOzlt+WQfwcXJE1NM4hd4rDY1Bk76+SQ8AwZlrXpqgttkDBxNQR3R+3r4rbOq87OhU2Rv+n5DU2X+msFnkM6kj1NYSs9VLiVRFPpMa7G/zWuiIkAWRplLnE7lDHABDB9q/Fj0iZEGkZba5U9My3SNOWspTAFD/V/s6dy4O3CKR5QFm6hP+neEGcZwjvr85uMzmXnGGxsDQKv7Zf+aaZamN1wAgIeXi+hhn69nXifLLdQAQ/ZT1y2PeVl0iUdED1QXS3EH5LzceOBu76EiRuMi5sAq9Ml8/h4847HF3i9u+awWA4LnSVejbL2U/+raTh3LTBvId8bgP0NHwXav5HeDG7t8VAEjMzZqtsiMRkQfgFXivdGfd5j3rAEgmVSpvFnO0MGUWmhoQHAoc25bz+mmxeFLk0AVvzctMSPwsD1My7FdR6tk6z1qTdhSWyph8Z1PnWWvSbgNBgL6ieOnm6kqbK/mrre7l691EyiKTL+aaGqWiOHAzjhamzEIrwjSZucuwpWbO2sRoU6JxYpxWwN8mkbvEARCZfHFRMAA0fTElrX59YcosXMuvGJiWoMnM/QUAVBTbXhVs0k5Js57S7DZxJi1elic0z03bGdqaxcvyEuTioOm9eXtsZtO7SaJJQp31t+zGVCpuZcz7L5tFp9wkjtPE97fPC1MmAAYgoC/f35x0Z92eL+eIbw5NDQieNSf4LxVNP0kIjk5I+awwRbKl8I6ty5j3obmZ3CXR6Q+njFr2mfCAhoSUlkK/5f327gaqCrSa+Ur/GCX6r9r2VW5itCRRx8PGDv3/tNUVt9+Hf3KyO19teH7j+T2WFdGEVehLl/ebs3vRkXM5lpulA/r7DB4QEBrgH+OH6sZWV2Zwdx/fYaOsX3jWL0LbVof2hur+T0rmHC34zc/+7Vf/gyUbtz67+bTVDu7dQD5Ax7374o3xAICrZUVnATw//9VoYQ18IiKP5WZvuT92wqrdwaNlJ96hM2utm1SeLlsqPvY5JExtw8Y9BU0AZr1peXxxZe2ddZsLd9SrP8HuTk0TgODM3KyWQssPAOBhKwD4P97JOJWny5baVUalzkHA9QL7Ja9ageDMt6wmPzuRSGwCmzimREAbut5A4pOuKmzmFF57r6AJCM78ufBMKZtEplJJor5qIKVEAKCrN9W5tXLzninHDYAvcCtjs+IaYN0S5+hUAOieOOLD4ZplivxG/Gp+MKBzJg7cpL+JcXQZxc6s9abGffqbC+oquvP9rcv9zdFbPWB+f/vJiJpl27THTKsLNkjnUFifosf7m9O9sXL/nikllqdsfAcAVzaVde4gPgNC/GJ/5jdE8gg02UT/tbxfv5BV29caiiw/y0sBaGKSAHxefVVh17iYUQolwNNRcQDOXr0KoMPQBsAnQHG99cSZr9jPqH76SQAHis9IXjI86mi4b2hoA/z8Q+Wvwj8dFQek/3pLQtCE4VY/CAJwqeZvUPvt/a3mEq4Zlf6iWy01F95voO0UMJ/oFF9YPRNu0uJlP+RMegIArmw6bXs8t2kgKwED/IMAJD0zuOlhnfQ7EtPj3/7Pyq7McFc8ryhprEahhIioG3EA76SKbampqanbKnr2LHeqbyqUCMv/uKSy7NYDAOj/D6qb1ZfVyC1+fuewtgmyzysybVDdpFASPkBxFrijOJXylbFVX3ajCQBav7Z95g0gPK5M5nnI6omUm0C4se2RQlknGkhY1KeppKbVpkBsgmDIPcXHVGqbqG8ayJqQyDYOAMAIQLih2jRUSBFv6AhO/KwwqyXP5juaLsRRWlEMnYsjPvRIuMc4IUWoszj/fPiIUYBw47F55JM3Xjz5OzJx0Of9zRRHk7dGY0pkihMm2wTq3KG/ucJQ093vb13qb0691Zvfkx87XfZ6Rk7gvJzAeTkbxaW9+ocCuHnb7jJ7T/a3zvTGyiuNAFBRHDjv8B+lmyktOyj/SwvxCQsH0N6g9JXZnWql+/19Nc8q/ftxtfqCQolJVPgz5j93/NAmKZHuq3kmCQDGRUY4OJyEuKbaYKuV8WTPa63hcjvKL/9NoVT47Sl9oDPd9w6IC9oBT/nYjt9tDE1bL0yj+Lvq12ru0EBmPkMGDYi5+XU5gEetd6y7q/nxby85OLIL5wUACEvqExH1Ck6hd0b94VXvneqVM1262YSEYPsliyaNCoGj1ZKz89Iyw6zmiHZhs064dL4J0cGoKA6UXJZsKcwS6gzgJ4pxvlmQl/WZ03WWM8DBh5DOE5rAJg4A4SLV9bp7o54Z7FIDmQmL+vSeLjSQM3HQy4lk4wAwr0/W5Ti9quf7W2/r+f6mopfe39Dl/iZ5q78jrbOw7wPTIorKBgLQ31RZfk/tvK71t+t134165gmXe6OwiN31b+6NCpY5xWhX+8Clkb81Gt+3SfT+bFR/7yOsC36o9Pi/TrN66NcN/VcAYlNnqTxIbJQmHjhbUHp8w7QZLe0dgM/gx/xgaLuh/wqYIDzqpP/omHFAOf7zk5PbJtk+Se77b6F+DVn5vO/O6ddiNfceQNCnlwBcuvkt8KRiE5w6Xg7AZg480PHgFsxrxQ8QrsNr2+2/SRHG+UOGWf7Nrdet+MnPuzgu7YUGAgD4hIYMCPWD/uuLAGJTbPcVHv8Wq1Fax99ZducV6WsuAUh681WuYEdEvYBX4B2qr9i2Yr+Tq1V1WaX8VdaY9fODAeivq3xcEy+t2F94SXtLEwQADypVN5skzIKuqLa+8XXogsRgqH5SVKgzhDo/UItTo1xnaWUUrxpNEqP5z3zL/nJTUChkZ7E6SKTUBMJ/LldVu9pAZtVFFQCCM1+0/YgeJlwsbYH95H9LqW2iHm0gJ4cHQiK571K+awGAeu2UeeL1w8B5xV8KRU3aKfNyAm3v1HU5jvjr6nqcyv17AuflBG7TAcL1w5zAecXHhDKrIOJPxkUxz3sycRwn6un+JsaxSmQdp3M3S7tDf1Oh+P425MXufX/ran+THFZa5xjLb8n8ngxN3nrZy+ky83R6tL9drrrsTG+ctHhZS2FWS4rN+1vo6wkAmj4slj3FoDflf2mNHfXXAPgGKT5pXKm/hT7uE/3qm5NgO48dOL7v3bMANNFKK4kDwMhX5sea9hWf+hbgN8S0byAA9Bv0+JzX0gGgoPS43QEaAOAZjd0Q1GfwYz4ADO3yS79Jzyutc87qcgAVn15Va4LbPjZz4AEA1e2XrgFAkPANWoxvBAC02y1f29GgBYBBzy9bL4zetVMyPtwrW0sLt2gg4dp7qB/QVvTOLz+X21dYfz5e7maHzlFvoHGjOX4not7AAby6+sOrVvTSxXdB7dcl9QD+//buPj6q8s7//zuQpAERBK3STRCGJjZSpHYLBpLCmtZWQwVvYPmt7oqL7jcU7MMk7SJdY/vr9iu6kt82CV+XLOkiK/ahXTfeEEqCtS1qSkzE/XrHYmqmJiizRqsxFoQUAvn9cebmzMyZm9zAnHN4PR/+IZmZzPWe68rM+cx1znXlbd58/Sr/guT5D2xeco0kX9t9z8V76LbH2ryKWK/4glV33x5Y0e1I7LtJmr6qQFLvpsdM3+t78gNT350/ibPTklWbJV0jyfdaY9w4cdscaoz/bpLysqPafFwWcWTUkxE7PyeVKEYXyGhzw/A7KCjGKs3TK5dNkbR7T6dVosCt5kSnv4OS5E8kcwdlzvOfgRnVC7GMIE6gg0Ynzqixx3gbNbYZb3HEeH/T9FyN4vvbKIw306/V8/42+35uPLbTKwXe3/p7JRUsCe7KkR3csat9b+Q+CKd7vCU3Gv0Fm+n97bOSdOGfGW3eG/VLJGn63OgX7WjHQMsDAx9Lmj82zkZlMcbbeZJmfPOquZK2fefaO/d2S5K6n3302hufkDT3gVsj58zDzbjm6uBjOwf6BiTvs0/fYDz22mmSNPYcqaTi//uypEduvHXdZv9TqKfH/yuuWHdH2CStMtKNWWJp4P1YO8mZn9fU5npJk5eM7YvbBa2z0iZLajve8uSpo8HX8KGTxmMv9D+D/1r3P/ZIMm0j998D3ZKmF/3TnZsLjOo90fu2bTpo3wdZ07Okjsa7F1z3uOVjuzs7JemL02fEj5SEOB1UyPr2AM6QtMHBwVS3waZ87Q0bN0RMvRdXNlYMbyscs7lz50p6+eWXI34+7roqKXyJ8pDIBdX9a62Hn5Y278rrX6iIXkLlSI8mmNcrjnG3WA7vrv7X0PmoV15/rCIv8tM9TpuVIE6MxvTurt5xw3OhY9B5q7/9wuLojcp6d1fvuO/i6/wrHierd3f11niJrONoU3nV+q4hdpAi+8gfZ+Xtlm029nCOdWsMp7eDwuJ4e5Q71TrR3WUvFFhMwkftSu1fFfw0xNGm8qrRiuN/Xv9Nxq4H1ge1q+4NXgYfS+rHW3gixYvzz+s2J5hDssV4s4gTuNuZen8b8XiL8WvNv2d39Y7Gwus2F9jm/S3Z0Xi7qc3Nq9MW16u06dCq+402hP0S49Y48sfOvy0jUHzqgyf729o0eUnYsufWrTrV03u0p3PzrfPXR11QfcW6tl/fPCP0773rzv3OI9Lf7hzcdq36+1/9ZECSui0fW1jTubcs1/QUz9x5683bTHcrbRrcEreOO9X3iWl9taysyyela+B4aF36GM+bftX16eMTdcHRlhO/2nky2Aj/qzs9/ao70seb7v7Bkyfm/HBgS0nEyz92fuMvXgyskeHn6A6SJPX3v/rU2i/fvE1zH2j8xdo5kyZMz1LfJ0fClrgLPsUtkRcvWEjueWMd6QHAyDEDb8XXXl22dE2gevcUryz2JHjEaOpquax856b20NJJ3va2teGHR7Hse+7pceU7d/uCj+31tretLd/zaoK7xWAsd/zGc4mvJo1qsyR/m6Nv6u00xzEa82poaWWjzVvN1bukfb/rkaRj5rP+ereVb73huQ/3bd+6KJk4pmdPkMiqCyT52zyCDgrat33rokfeC//Z4Weq/bVusonOSAclaV/rQSmsg3p8nZuqt0VttxbbCOKEOmiU4owae4y30WGn8RbHGXh/G53xFvXYI8fMJYX//W3bfVsXVXd6A6vQy7wKfbTTPd6SG43b7tu6qCliH4ff3RX8BsFqeFi4rLCwtKapYWOoOEw6kaSDvUd7Tkgz1j7c9uS6W64I/rzw9qoHw4vDmKIeO/f26iZT9f4H4ym+tsm425zA3WKuX+D1ev+r42DE6uhJPO+cWx5o6tzrr8Djd8H4hRlX3ZY+I7RO/dgZSzIjqndJF96YMXNq+I8uSr9qY8aK/PDqD3nf3AAAIABJREFUPS6bdNDfLgg9trSmKaJ6Px1idNBpf14ACGAG3oKvoWxNsHivvKuiQIEfnJEZeDuZt/L2F3L2hk1/Wc5QRTm2Y12q4hiTXZarWFnEkd0TxYmjs6GDkosjW3YQ4y2I8TZazvbxZjnBG+XYjnWvvj/SfROHZ3KMCV5J79Z/fWnn9yMneKNn4K1cftGElHTQqdfvOt6tsfOtq/SzooOSc/lFkd/bMAMP4PRhFfpYPJ7im++qKMiWJF+iO7vW7Jwpw1jo2LZcFkeuS0Qcm3NZIuLYnMsSvdP50shXQbcVOggAUoIC3kL2grvqlmef2a2+bMmz8LsFvU2PueXj2WVx5LpExLE5lyUijs25LFH35vpHrri6baSroNsIHQQAKUIBbyV7FKp34+ypOD78MOxj74ILLhjxc46yeQsv8T6xw/pizuz5L+yYH712USolWhEqXhzZL1HiBa7Opg5yWRzZLxHjzcxlcWS/RCMcbx/vPN6402JtthTKyMyfkpkV+/Z3f/lE3o9/GvPS7vTM/Isy/eu6nTgd7RuiwGJ4cZxdHZSEiIM6ADh9KOAR077tW29IdRtGkcviyHWJiGNzLktEHJtzWaJppb++J9VtGF10EACkCgU8hui5p8c9l+o2WOpquey6luE80J6JXBZHw03ksjiyayKXxRHjLcCeiYYd58Ibs5beONqtGQ0njne8f3w4D+zvfzX+ovQpMX5hxtKFGcN4oPs6CADshwL+dImz9Khxdr0Nz5kHAADAUHFQB+CMYR94AAAAAAAcgAIeAAAAAAAHoIAHAAAAAMABKOABAAAAAHAAFrGzK0/+AzcV3VkwxfiXt73tJ4+1bBvi/r3zPPnLb7vq23PGZfp/0Ottfyv4e+ZdufDuFfOv8W9537v7iTf+853P/OWKS67J9j+pfL3eQ29ZPO+V1x+ryAv8w7SxcHib37h7YeixQ4pjbBHsa1u0tmVf+C03/93tdUumZIZ+cPid9v/+p8datnVp3pUL7755/jUXhW77bfW2u3Xp3cNOFN5mSas8Cj0w+URx4iy//h/+Mi83tDVtKI6kectvarolZ0Lgtp725ytbbd1B8zz53/7uNX91cUbge8GTR3xv11Y9fW9wvIV30CtNbe9/adTiyNxBjDfGG+MtfqLTP95mz07/yrsNbzzS2ur/Qf7EGXn9uQvHjJcsxlv9kvJfvPK7109+fND/o8nTx06aMzZw/5APnuxvawv8w7TTeHib8ydlvn/0+MfGnurdzz76L/9U9chLxk1zbnmg9I61RTOs4iiwYfjA8Y6PjkcsDz+p9/mW7i8UluQGdrA/9af+gZ6jxz8+oXd/s/nh6vVP+F+CwtKae779t38xTulTz0k/Lz0wQgdO9Q8MhFoVlJV1+aTgwVhgK/jwNheW1pw6qtBLkXwHGTu6T16SuXBhxIzNwDtPDv6hZ46v+xX/Dy4aO+OK4As+r9DzD8uWl/ijepsf+c0PN9u9g7L+Z8vKP//7fYU1nXvLcnWq79PjPUcGjPtYddDCtJGs/x/VQfesKwu8XABweqUNDg6mug3252soW7O9S1JxZWNFwYh/nbEKffQy9eOuqwr8b/5TO5ZcE/k406FkQp78p9YtCRTnkbxPbLtN172wbIr1zdH3b9952X0doX9bH+DGafOQ4lzwwOZVd2ZLEUeEnvxn//eSr55r3cJ33vzjxZdOjPzpSWnssBPFb3PyiYYTx3kdFHe89TyxbYWz4ojxFk/qO4jxJnuPt6Md5737UM/vrJs3dvb6wh/dFd5BzauLFte3xrj/jNsy5uSH/m1dH1q22SiGn7136o1PRN50xbq2X988I/rZxkw9f/zUdCmiPsxI//x5WefGOGfx8G9XTlz4SPjPSpsObim52Pr+/f39HZ8MhP5tUcBbtjl+WMsO+mig5YGBj6WIAv5ox4nWh04etW6eIztIyprw3jPfvKS8VfIX8JKkgYGDH/W/Xv/1pT98KeoZVj3YuKnIOlNClm02P3HMIz0AGDlOobehCx7YvOQaSb62teVV466rGle+c5NP0pQ71y2cl9RvyH+qZsk12dLhwK6nvrZFxu9p75WUu2zVr5ZNkXp3V28bd13VuOu2bXo/8NA3nh93XZX/v/Jta5/olZRbsOSNlVH7oxi/8zrjiCGyzZICbf7m1qHEmbfyujstjsvzn6oxHd0G4uwO/ODiSydKfwzEqVr7hiRprHTsvbXl24aeyKILpGCbh9BBieIEvj7ztS0yxYnooLXtpofasYMC4y3iqTf+X68kaWrUeHsmeNg1SnFMHcR4ixOH8cZ4OwPjrePErx7q+Z1U+L9qmjo7BwcHD724sKyxubOpprRQ0sn9Dxw51Gsab95aozgsLG1q2JK1dKP/v6vWZ86eLulk90Mn3v4o8kkmL8lcujFrqVEcWoy3ngFJY6ZOfPfxa298QtLcBx5sO/xKz+FX2p5cN1fSS1Xf2fxuVNOzJmRNtTgxMX3GlKxzx0je5trVRUXzNu58/8irvf19gZvP/er2zppblj362is9h1/pOdy9/4Wmzi0lF0vv/c/B3qOvvn/E/1/v0YOfnpKUlZWVPyHq6GvgeMf7R159/2jPibcfjWpzTaGkgwP/1XJqKB106u2fD3xs3UEnj0ozbgp10FW3Bb8Oiuigz3jS0ibavIOkjMyT//a35cZXDMf/9Gawg9LTp3/4r/f88CXpClMHNZVKemnbd9Y9a/W7EorVQa3lt9Z6h/UbAWBIKODtx3Pp4mxJnWvXBs6I6+pYv3bnbknZ8+++MuHjA5+U7x9659zMsFu6Otbft3XRE72SMqWeJ3bc8NyH/me8SFJvj6TL/mxV6P4fbtu+dVx1p6TcZV9dpdii2ywF2vylpcnH8Sx8yGLeLJAoQlfHDeVtpg/LjxuNOGbjjqjL9MMkE1l1gSR/m5cn3UGJ4pxQmlWc44roIP8JivbsoECc6HMR9/76svK2d6TIOJ5Lv5Ql/yNGK06ogxhvMeMw3hhvieKMfLx9NNDy0ElJhTUH9taXBc827+q44cHJ39qyt7Pmy5JeKa/aGuygQ7seb5V0/b/t3VKyrDjU7PHnj5l5R9b8+ZJO7n/+VKw4lomkno/6+yR1//JXL0ta9eAvAqdkz/jGzb94cpmkl9c//Jvw35OROeOc6IOiMVPPzzpPkre2KG9xeX3gioATA929x/ullmavpNyy//PPX5vpf0TuwpJcSc2r/+yeX5wwNfzEqY+PHH31kwFJWedkTo4Vp3v3M1FtLtvbNFbSxztPnpd0Bx1tObn/oCIFOujcG37V9Wiog8bnZ1y1ZKwknauIDjohHZaNO0iSJr9XV1oefoZAoIOaN617XZrz45/eE+ygz5ds6ayZJ+mRnXvjNDuWGB1UKqm1vKp5GL8RAIaGAt525i28JFdSe8e2sB933GdMrVwcNa8S4cqv3pkt6b1XL8q5WL2bqtsivg/et33HJp8kHQn+aPr5uZJ8H1l/c/xcx25JOj/fM8w2T0g2Tv5TNfNzo9vsTyRF39T1B68kHXzGJ2n6d4OTTuNiNjWZRDHiyGjzrLn5yXVQ/DjvvSplRNzqj/PeMxEdZMTptWUHBeNkybKDDgT+1zzepkb8ZHTiiPHGeGO8pXi8ffD8wMeSrv7x3rJLrcZbbtnGosmS6lf/KHCG/bsHWiXNuCBNVi6cNVaS3jsV43zv2IkGej495d31+D5JtywJP1P6G7f++ApJnd63TT9MnzElM0unej4Jv7I6K3NquqTm1XmPFj36wBzzTSdO9UsLSzyfDEiadFFwUt1I4n1rv2Vz+wf6JGnMZzKs47z7yydet2hzydjZ0yUV/MXM5Dqo48Svdp7U9PT5S8KutAh20B+f/HpEB43/rCTp8+kRHWSc7H+uXTtIkp793uzyVl2xzqqDSrac6O45/HDpzLBH5F7yxZjtTSBWB62rKZS0/y3m4AGcdixiZzuzc6ZI2t3aEfHzfe98JE3JnX/pvO2RawWZrSrMk9Tj+9zl2fI+sWP9wUsXR97lw/VV2xq6Poz8JdlTvyrJ91HUMUfHDddFNmZIbZb02yTirLp7yTWyaLORSFY36cr8ayT53ttQ9ZsN0YkkvR8dJ3Gi+HEunnFenFuDieLHse6g+HGm2LGDEow3I9H7r91e+stHo1p1gf3iiPEWxHg7/XHkuvF2qqdN0tSKB38Qfas/zoQxX1mfOf78yNmD7sOf80rjohPlZyzdGKPUTZCof+DUewdaJa0o+UbETdNyvyi99Hrj7ndL106TJE2elHWe1P9pf8/J9PNM95z8mXRJ3tp7237804dn7n7G/Fuy0s+TNHDyvT/+6b0TpyKrytxLZv/1n6yugh/oft/62yTDO50vSVoW1eYxEz4nHZydc6Fl2IgOOvX6QyelsbP/Kn3CAfOSefE66IMDJyVNnjbmK1dbdNBhm3aQpL3rbnxCmvPjn95s3UGnjkW3qfnpf5c0Nz/GMgXxxOqg3EtmS62tj+/ylpWxmB2A04oZ+GRkL69tbGxsbByNFewSuSA/J8YtB2NMiFk9fGq25Gu7bXvUKZeGiGPB5377aK+ME+eOHNNsT6JJ/hhPauHg0d5YD4qIc+X1mwss23xBvnF82xt507wrr3+jIk/S7sdb9oUnCl2VedElD929cNXQEiXqgliHKuZEceLE6KC82HHkv7rQhh0Ub7yFOujR8Grqud/uDSzOYLM4YrxJjLcIjDfTrYaYcT469YkkLf1GbrzxFlEcLjBmLv9jcV7RF//yvvajUVdTDzvRyc7/tp4El2bmh03VZmVNz5IGjncfiTgTfMxnjktS58fX3Rs+hZuVlZU/KV1S36fH+8Or94PNP/6ZV1LJlp/9/TcmZU7OGNKB1tsH34xxy4TPjlXhrM/HuNXcQR88ebxbmrxk7Mzzw+8Us4MGP3jyRFubpLFfWDgmooPGyXOR7NpB0m/u/M4j0twH4nVQ+CMOPrO6aHG9pGXf9X87MBSxOyhvVuGQfxsADAcz8HZzwSXZknrfir5yzZB9/mwp9gy88XBJvZuq4k3UB626e91m09cSE3Lnb66Zv1n+HYmaWt9cH33h5RDaPD5Tko5/miBO/lMVeTHa/KUVUySd3PdvppumzH9hx3xJUu+m6q3rn7OOc+SIJkyYklswf3PBkBIl6oL0uLdmnz9b+XfHjGPRQRMkZc/fWmERR3bvoBjjLbkOsl8cMd7s3UGMN5uPtz8MfiypcFbeEMfb3qYDRYvrW1sPvChpp+Tfoixt6qz0CyNK0CEl6uxolbQgf2b0TZKklzvekaYpfcakdOlUzx+jz83e/87EK6bqnc8sW/u54M/SP3PpRRNyJelUzydHe0yP2bvu3O/4V6NPm/fHH3zh3DFZWZnTszKny7+HXN+fBnr6410tLv3+7ZclXTHTus2zL/l8rLCSjA7qaG1rk6anfyVy3zjLDpKkY+2n2tpOanr6/DvSLwx7wKq7120uWBdYhd6GHfTsvTdvk65Y96CpFE+qgwrv+89nShYmaruF+B0ktR7olJiBB3BaMQPvNsbVnr3P7khuw7kL8tXr9fUGvrk/4vUGFtbNnpJbMP/OilXHdtz+xsr85Fa/H6bQ2ZhRbV51959PlaTDL/6P+cf97/jbPOXOitufujI4AeWP0yNJmjCh97ePPG8svC+duUTzYseRRQeNy5Q00P+ORRwZiXpC5//ZroNijLekOsiGcRhvjLdRd1aNty9Ikv6we6jjrWTL3sHOzv9YvSBQ+Hx88GT3zoG2B/ob/+XE2x3xS94RCp2bfSLypr3rtn4kSRfOCltsf/BPA8aU+5ipk8bPyAoeR719UFfMmXvFn0vSD/MnFvx90zuhcj19TFZW5tRJ4y+/aHz+hPSs0xZnMHjy/HiLW6M66JgkpRnLKhwcaPuXEx+YJ9gvyFevt8vrtW8HBU6en2H+cTId1Hr3X3793t+8LQBwIAp4V5m3svhySTr8s10Jp5UMH66/b+tla7dedl3Vouo2rybk5p63u7pqXPm2RdVtm9r9B5G5y5a8sDnJHeyGLHvl7ZsLpPadl0Wd8J9n3CRJ4YcHva/+TajNU66pWPXUlWFxftwuSUc05au3/MUlrVuNDZbOVKLpq2LEkXUHHeuV9P6rf2MRx5/ox69L0m+bbNhBMcZbsh1ktziMN8bbmYzjvvF29VclqfeBHcMZb8pd8a+tnc0/2Zh11frM+UvSZ0wfK0kHT+5/6HhLy2kqETMnjJ+eJfX3d0Sdm/1O/dcDs7Xh+7kMHH/7o6Md7x/p+OR4v8acN2n8DH85PvPmql8//ItfP3lssLOpZp5e+udvTf/OU0eNDeQ6Pjne0++vKrPOyco/P/P01PDvnOqWZtyWEXnyvGTdQeMkKWvO2KUbM+cvGauDJ9seOPFB8BEfrr9v6xe/lpeXllb0j3btoFuejFygLukOeuLmL90bsdA9ADgBBbyNPHWlpA/f8kmacsn0GHeyWIQpwLPwoWUTJEnnxnx4bPuea7ntiV5J16xYOK/rw33Ptay/zziI7PQq/g52H77VK2nKnTXrju3w/xeIc/S4JGWeE6s974+7ZdkUqXPtfVGrLqXn/L/LpgRWYLZ+QcLaHHXrq+2mm4aQyN8F5jj+RF+RZCzJG7uDlGkdR4k7KH6cI7+zYQclGG/JdtCI4xiJGG9hGG/hGG/+OKdxvP35rEJJHYNWa7QNYbyNP3/MhQvT59yRsXRj5vz5/u3TPoh6TDDRz1anpZ1fVntXf6P/v7S0tHt/Iykvv1DSix2x5lnnfbHwnDHSwMFPBiJv8v6f7//wJWnqZwckjckaa/Ho/v7j3Z+eknTeOZHVeG5J2Zb7r5D0xE82vyvpxKn+/uM9nxhV5UC/pPTMqdYV/OdnzpVKf7SxYMLlF4X+k/TUVZL2v/V7xesg7/79mp85J9/ipkQdNObChcZC9yd/Z1GNt++2ZweterAqcjG5oCF00BB8fuZcSS+9HavNhbPyhvYLAWDIuAbebj7sOGRepMjE2OwtNv9OLZKkayrWHasw3ZY9/4Ud8+VrW7S2ZZ+kK68/VpHnbW+77b6Wfcp/aseSa9S7qXzr+pa3vMvm54ZfZr/vuadvu/j2F5ZNuaYwX89ZLm78YUevsRhzdJvHW/44FGf8BRdLUt7mHes2R9zhopyLJWn61dlRicxx/G3+4gs75gcTlRZI0tQP3vIqMk4SiWJ3wfnn50o6EePvJtRBVnGy579QE/pXzA6qihkn9wLts2MHxY0T6qD5j9+tFaHxJklH3nrLW2C3OIw3xtuZjOO28XbJbKlVBX91w17zOtwR4+31mpbG/xg7Y8nYOQvHqOP5+WlpKlx99/X7DlqMtzEX3qjZ753cf/BkT0fGhVZ1qT7siHXC2di8L86WWq1uervjdUlpYz8jSenTL5oQURF7n3nyFUn68U+f/ce5Jfrw36/+cnCb8ZfXL526XnMfaPzF2ml/Gug/JzMrPT3/osz+/uPdnxzvV/pnsyTp0uuWzfmHl173X8Ud0t/f350+Pv+cMed9Jl39UXWpZk6/NEac/j+cVNuB30uW69iFOqjteGNb5K0f75zr+Umuf6H02B30ty2t+w+e/Pil4407gx10stt4dus3hFR30LbvTN0W+ViLDhozToq4ft5ztXUHJTJz+qXSy1a3dB6wjAIAo44ZeBu54TlJ2n+oV9I1hZEfhPMuPl+St+3NZJami+uCp7+dJyn3/D/sk4ITMosXDnVx5pD9r/VKUvvOcddVGf9JuuE5f5slfTVGnHcOHddIjZsiGStm58qf6P1jkjR1RvztkmMyusAcx0h037HzJb3T3afYHTRKzpWpg/xxLnFqBxnXLU8NG28jEh3HSMR4Gy7GWzyMNyU93oy9sFvLq5pj36f56f+QdPJznx0jSZ+dfJmk1pYDQ13bPGT/tPsHBwcH2xqzlm40/hscHLzna8pKH2OsC/5487MRD3nX+9+SvrzsWwkXG6u/t9Yr5ZbdU2p9++8fLEpb3TxGUpaMM+RPnTglSRmTh/kXdHHeFapfnLbyqVffPxL8T9J1De9J2n/oA8U5Qtj1ePwaMokOKiz9q/dl6qC0LEk62WfTDkrk9w8WpaWlZdw9eqfKX5x3haQnotrsfWu/pMIVI28zACRAAW87+1re8koqyF8V9uP8u5dNkeR9J+bF7fu2bx13XdW467ZtMg5YfW2LrqsaV97mDf7/2pZ90rwrv3q1ceDnX6rqw4a2Xkm5y657+vb5uZLaO8K/0A489aHYTx23zUdi33Rg9xbzQaT/P3ObzYkkvdEZEWfVTV8KTIId3vSYMd304cu9kjThsi9ZxUmcKEYc+dv8ckecsLurhxKnfWdEB82+aX7osz/QQf44BfbsoEAco7VRHXTPd740ITyOMd4kXf4XoxtHjDfGG+MtxePte++9NFlS/eKiWq/VeFPz6sX1kgprvne9JOn86VcVSjrw9M+/stCygzpO7j8oaeyEz1rGiZ0ofeo5Y3K/tWKepEd27g276dmHf/iSpJnTPmcukv3/9R7vl3Lv2PNmz+FXeg6/8uT/mjAgqWTL4ODg/qo5kuY+0Nhz+JVfrJ0mTR5/acHtTVtKJB3uOWpMp5/69JQkjZ266LZC6ZYlRZHtTZ96zhhJ/SetLxuf9s1lcyza3Gy8Du3Pvx3vCOHU9cEi2f/fVUvGSpq85OXfPBXWQWlpaZ7bIzqo3bvzpFrrfy5zB+kcSVK3XTuo5/ArPYe7jw0OxuigL86WpEej6u1nN61/XZYdlECsDqoqb5U0+xLqdwCnHQW8/XS92eSTlLd58/WrPJIkT/4Dm5dcI8nXdt9zCR//4fqqNuMayBc23/7UV8aFlt/x5D9w9+0vVORJOi7psiVv3J0/T9q3fccmn6QpV18m6cijzwfPurxg3pULnzKeWp0/ibWrvGWbpUCbX2scURxTIkmX5ZkWq7lg1b1rTHtEnbv4Jv/ay75DwR+e7B2neZ7QQ5JKZNUFkvxtbhilDpJUsOQP3/3cOVZxzB0UiGPPDgrEMb4VuiwvdKKj54qnt323MleSTipsvD1jTCFePHpxQh3EeIsdh/HGeEsQZ+Tj7fz0rywZK6m1PO+7/3T9F44E1te/YNXK6V94qChvcb2kL9c8XLbM30GT/p+HawolHdy7vKho9eM/rAt20EenPmg50fLQSUmaH7WledxE0tTzs86TNOObV82VtO071965t1uS1P3so9fe+ISkuQ/c+rWEcRS2e1nebQ+WFgb2+h4z+bxzpmdp4W0lkry111xd/qxxNfWJgXe8PZJyy/YefL+mJCsj+KvGZGVlzjAapoH3o7c0N8y45uqoNtcWLT4pafKSsX2j1EGSuh/qf3F/YHO3j079V9Xxbv8/zB005lzjZ7btIEn6+OjxfkmfyTAtvT9m8qTx07NUsq56rqRHbrx13bOBy929zauLVvy7pCvW3RHzEvqYYnSQ8cXUupIh/z4AGKq0wcHBVLfhrDN37lxJL78ceRGVcR6jJHkWvlEzP+pb3N5N5VvNO/f4d9Bt3zkuekkhz8Kn1s2/xvJCR8nbvvO21nyjkk/KsffW/sPPtgWf+srrj1Xkha6oT9hmDTHOY599o2Z+btTvf/aH878a45LTd7w9F+dOTTaOJF/n2qqn4yWK3wVD6qDXO71z8oYUx3kdFHe8HfE+v3jnnzkpjhhvcaW8gxhv8aV8vB1tOc+7s6fbunVjZ9xWeOf3wzvIW1uUVx7r5O/Lrs686OtjgpXZB0/2t7Vp8pLMhaZ9zq3bfKqn92hP5+Zb569/PfKmK9a1/dq895h/h/Bbnnyl5lv5UzKzBo53fGTadTwj8/OTMs+1WspO0uF36ydevDr8Z4U1nWGXmYcZGDj4x/6Pg5uiZWVdPild5mfstmzz9PSr7kgfP8QOaq1OL/ruyYiX62jLifadJw9bt86RHSRlZWXl/+FfivLKW8Ne+1N9n/S3NH1n6c1Rp6roimWP/vSer4W+NAs9ReyF8fysOyjsiWMd6QHAyDEDb0tdLZeV7wxt8Ct529vWhn82J/wNN6zdtqi6bff7x0w/7fW2t60tr7rsvo59zz09rnznbl+v+aYNG3cGNiLy8+8I/frL2xI+dZw2R92k3s6hxZHU1fKNVdsW7ewNX/PnWM/rbWvLq77wvUfC40jSq49ULSqPTCRJ3ucXrX06QaLoNkuhNo9GB31j1bZFj3T2DJgnYfxxrDpIR7w27qDgeOs1X/R78njvoW0bqz77vZei40jq2DmacRTsIMZbrDiMN8ZbwjhWtw55vI1f2Ddnfeb1f1cyd77pp5dNnb0k86qNGXPyozoo9/aHX7z51tsCO5P5Tb2stKaps/Ond4yx2tI8fqL+/uMHe4/2nJBmrH247cl1t1wRvKnw9qoHw4vDxE4c//2HRzvebnvBa369B08cP36w98jvM29+pe3JZXODT1FYWLrizw4f6ejtD2wd5+f1Ntf+6687PjJV75ai21xak2lU71Zhh9FBGcXrM788X+eZN2CbPDY7VgfpW/9k7w6S+vv7X/3khOl1PWU8RXf/qWlf2xTVQTWP/99fm6v3obHqoKY4X9kAwKhiBj4FEs/A28m8lbe/kLM3bJLfcoYq3LEd61IYx5h52F1dZawLaGYRR3ZPFCeOzoYOSiKO7NpBjDcD420Une3jzXKCN9yxHeuMhd9SYvKkCdOz1PfJke7+yJverf/60s7vR07wRs/AR7n8ogmp6qBTr991vFtj52/MuNDi1rOig5JgbPVnxgw8gNOHGXgkMDtnSpzlnRzHZXHkukTEsTmXJSKOzbks0TudL83Js9wFzqnoIAA48yjgEZdn4XcLepta3PLx7LI4cl0i4ticyxIRx+Zclqh7c/0jV1z9zeGetm1DdBAApEJ6qhsAW5u38BLvEzusr6zLnv/CjvnRC+ekkvWYruZJAAAgAElEQVQyOSHx4sh+iRLF0VnVQS6LI/slYryZuSyO7JdohOPt453HG3eG1nWzg4zM/CmZWbFvf/eXT+T9+KcxL+1Oz8y/KNO/rlv8K+TPjKMtJ36182Tcu5xdHQQAtkEBj3j2bd96Q6rbMIpcFkeuS0Qcm3NZIuLYnMsSTSv99T2pbsPoooMAICUo4DF0zz097rlUt8FSV8tl17UM54H2TOSyOBpuIpfFkV0TuSyOGG8B9kw07DgX3pi19MbRbs1oOHG84/3jie8Wrb//1ViL16XQ+IUZSxdmJL5fFPd1EADYDNfAAwAAAADgABTwAAAAAAA4AAU8AAAAAAAOQAEPAAAAAIADUMADAAAAAOAAFPAAAAAAADgABTwAAAAAAA5AAQ8AAAAAgANQwAMAAAAA4AAU8AAAAAAAOAAFPAAAAAAADkABDwAAAACAA1DAAwAAAADgABTwAAAAAAA4AAU8AAAAAAAOQAEPAAAAAIADUMADAAAAAOAAFPAAAAAAADgABTwAAAAAAA5AAQ8AAAAAgANQwAMAAAAA4AAU8AAAAAAAOAAFPAAAAAAADkABDwAAAACAA1DAAwAAAADgABTwAAAAAAA4AAU8AAAAAAAOQAEPAAAAAIADUMADAAAAAOAAFPAAAAAAADgABTwAAAAAAA5AAQ8AAAAAgANQwAMAAAAA4AAU8AAAAAAAOAAFPAAAAAAADkABDwAAAACAA1DAAwAAAADgABTwAAAAAAA4AAU8AAAAAAAOQAEPAAAAAIADUMADAAAAAOAAFPAAAAAAADgABTwAAAAAAA5AAQ8AAAAAgANQwAMAAAAA4AAU8AAAAAAAOAAFPAAAAAAADkABDwAAAACAA1DAAwAAAADgABTwAAAAAAA4AAU8AAAAAAAOQAEPAAAAAIADpA0ODqa6DWeduXPnSnr55ZdT3RAAAACMMo70AJw+6aluAM6oDz/80PifCy64ILUtGRUuiyPXJSKOzbksEXFszmWJXBZHrkvksjgAEMQp9AAAAAAAOAAFPAAAAAAADkABDwAAAACAA1DAAwAAAADgABTwAAAAAAA4AAU8AAAAAAAOQAEPAAAAAIADUMADAAAAAOAAFPAAAAAAADgABTwAAAAAAA5AAQ8AAAAAgANQwAMAAAAA4AAU8AAAAAAAOEB6qhtw9po7d26qmwAAAAAAcAxm4AEAAAAAcIC0wcHBVLcBZ05w2v/ll19ObUtGhcviyHWJiGNzLktEHJtzWSKXxZHrErksDgAEMQMPAAAAAIADUMADAAAAAOAAFPAAAAAAADgABTwAAAAAAA5AAQ8AAAAAgANQwAMAAAAA4AAU8AAAAAAAOAAFPAAAAAAADpA2ODiY6jYAAAAAAIAEmIEHAAAAAMABKOABAAAAAHAACngAAAAAAByAAh4AAAAAAAeggAcAAAAAwAEo4AEAAAAAcAAKeAAAAAAAHIACHgAAAAAAB6CABwAAAADAASjgAQAAAABwAAp4AAAAAAAcgAIeAAAAAAAHoIAHAAAAAMAB0lPdAJwRvvaGxx9t6VZXV5fH49GMhTevWF6QnepWjYTLErksjlyXiDg257JExLE5lyVyWRy5MREAmKQNDg6mug04vXwNZWu2d1nc4CmuvKvCiR9pLkvksjhyXSLi2JzLEhHH5lyWyGVx5MZEABCBAt7lwj7JPB6PpK4u8yebZ2Vd7XInfaC5LJHL4sh1iYhjcy5LRBybc1kil8WRGxMBQDQKeFdrr166YY8keVZW3hU6f8zX3vD4o9v3BD/THPSB5rJELosj1yUijs25LBFxbM5liVwWR25MBABWxv7oRz9KdRtwmvgaqh58rc//WXXpxNANE3NmLShZNLPnQEt3nyT1vbarLWNRyayJsX6TXbgskcviyHWJiGNzLktEHJtzWSKXxZEbEwGANVahdy/fiy1dkuRZuMDqm+bsgorauspi/7+6tq8pa/CducYNi8sSuSyOXJeIOGeuccPiskTEOXONGxaXJXJZHLkxEQDEQAHvfjOmxTxTLLugorGy2GP8wzmfZy5L5LI4cl0i4ticyxIRx+ZclshlceTGRAAQgQLe/brfjfsJVVBRa/48q24/E20aGZclclkcuS4RcWzOZYmIY3MuS+SyOHJjIgCIQAHvXtnTZkiSulpeTPAdc0FFbfC8sj0b7PuVtMsSuSyOXJeIOLJbnPbqMtPhthsSmRBHNo4j1yVyWRy5MREAxMAidi6WM6bnsZZuqe+1ozNvWpAT/74LFmW07XqtT1LfawfsurqLyxK5LI6cn8jn00RTK5weJ4LT47RXL92wp6+75bGeQPOdnigCcewcR65L5LI4cmMiALDGDLybFRT6v2PesyHxWWLZy4NfSXdtf9ymJ5W5LJFD4/gaqqvbracsHJpIktReXbZmTXgwB8fxtRt8pjwOjhPaHkrmM2SdnMgCcSTZNo5cl8hlceTGRABgiRl4Z/M1lP3gmSklsb5qzrk48BVzd0sSm6bkLJhpfH+t7kMp/kLa52t/802fz+dLS8sJmxN1bCJLDozjayhbs/2V7pZdPTMXLciJaoEDE0mS2qvLNuzpkrpbDvTMDP1BOTCOr73hB1Xfr/tZi2HXrscea+vJmLNg1kRHxpEUXr1LmvylawONcWqiGIhj5zhyXiKOEByTCACGggLeyXwNP7jv+a7uljZzyRFm4qw5obPEdvUkPqss8OnX98fzU/Np5mtv2Fr1/ft/tius/pg5J1ArOi6Rr71ha9WWn+/evXv37t0dn065eJL5eMNxcXT4wO7A8ZFlDe+8RKHqXZJn5f3/+E3z/sHOiuNrKFvz4PN9feE/7esOnFHqsDiSoqp3SX1TLg823IGJfO3VP6i6v67uMUNbW88Ul7y/Rb3DOS6OpFCmurq6tra23R2fTrl4VuB9zlGJOEJwQiIAGAYKeCebeLjDuOArzif0xFklga+Yk/lKeuKsKf6LyEzHyGeMUX50R5Qf6utu2RUK6JxEvobqH3z/wcbXuvsCul9r2bXLfLThpDj+BvgHnRSrhndYorDqva52eeQGRM6J42soW7Pd2AfZU7z0jr++dtEiT/qhP3b3TV5ZF/xWwjlxJIVV756Vdfdfe3RXS7eU5jE121GJfA1lf3NfY7f5K5Y+//ub/03BOXF87dU/WBv2/uZ/hwud8uGkOAajg4xMkgKZTEWigxJxhGD7RAAwPFwD72g50/x7oahrz4ayWNd8FVTUrXTAvqeh8kOe4pWVlZWVlZUri80Bgy13QiJfQ9ma7f7CUB6PxxO8pWvPhvCda5wQJyQ06CRpz4Y1FtfDOyZRourd4Ig4voaN/up9ZV1tbcXygoKCgoLlFbW1jY0RuRwRR4qs3muXZwdidL17KOyODklkeouTx1NcXBx8e1PXng0bHff+FvzTKTaY0mxfszTYbifEMZg7SB5P1Nt2IJNjEnGEYOtEADBsFPDuEecTOnt52L6nZbGWIDPxTDuzX0W3V68Jlh+NgfKjYHlFbaPpkzi0zozdE5niVNY1NtbW1tY2NtZVBqJoz6NhxxR2j2MW2KoneHBrWcM7IVFy1bskJ8Rpf9wYcMWVcYMY7B/HonqXQgXJntaIdzoHJAp7i2usra2oqKiobXT8+1tdY2NthaG2sbGuMlRRhdpt9zgGizdt/9u2OZNRxDsjUTiOEOJLeQcBQPIo4F0lzid0QYXp82zPhlhfSvsaHjUOmmdMS1gFjKL2auNY3aqOyl5+V+ADOuyw3caJwuIUBJ82u2B5beBgI2qnWhvHiRAoohbeFWywZQ1v70RDqN4N9o7je7dbklRcWBDjDr729vaG6oaG9vZ2n+wex7J6V+jbI9NC9EH2ThR42qjBZloK22nvbxbfFmUXVATf4wInG/knrW0bJ/yZI960peyC5RW1EV9MNPgckMgCRwjRbNVBAJAkCnhHO/SuUYEUV9YFt0OJ+wltOrTavmZp5LfSvvbq4Fm4K2LUAadFe6v/ePBmyzoqe/nNVke4tk0UL072goWeqAf42TROJH8R1fXuIfMRUqwa3paJElbvPl97Q3V1mV91Q0Og6LVlHAXfCqwmkXzt1WVLl65Zs2HDhu17tm/fsGHDmqVLy8oa2nNsGidW9S6FtomK+gLMuNWmiSTfiy3G0y5cEP0WV7BipeWbgk3jBN7fYj2n6SsJ+d8YJNk2jv+543WQ/4uJYCpTDW/fRBJHCPZNBAAjk57qBmAEArNunmk52QUVdZVas2GPjE9oVdZWWHwcZS+vrZtWvdFfvHTt2bBmjzye4hkzpO7uPV3+q/+SOgt3FJlyxLhHzjSP1CVj4q0gYv7KboniT4ZmT5shdRlf9/va241reXNyCozre20YJ5q/N7rf9amgoKK2Uv5qeM+GNaqsqwj2j8/ny862ZaKcaYH/szheD7sQVpLU1dW1Z/t2T3HlXRUFdoxjaum7hyTzM0eH8d+xa/uGNS3FlbV2i9NeXRazepdCbwVdLS/6llu0ypbjLVRJxZ3l80zLUeBNwf+WYMM4gfe3eFkKKiqL9wT3DtizYakqGysKbBknQpxQ2QUVjcE3u67ta8pUV7vc1ok4QrBrIgAYIVahd7I3n6lr6ZY8S7+9YtZETcxZsGhmz66WbsVddXZizoKSmxZl9Bx4zb+Wa19fd3d3d2BdZE9x5f0VBWd2LxV/DvP2zhGCS59b3cVuiQIbrYUtlR3ka/u5ceuhx4J7dYct3Gy3ONH8vRFYtDdnQcnMnraW7vC95XwNZWvu+1lbxqKSWdm2SxTaaqjvtQNhqxS3Vy9dvyNykWNDX3fLrraMRSXzL7VbHElpvjaLQdde/TcPviJ/s1be8dfXLgosTN/nT9TWc/nfVfydjeLkLJjS09bS3RfruoaJkz5NtOuTHf+CfG3GG5j1m4L/HXDyH3fV1e0y746VMWeB7f58ghtJehbFWbQ7MCD9QquB27F3lOhdO8j8ZhfYpcymicQRQuBG+yUCgBGigHew9v+8v6Vb0pevXe3/HE7yE1qaOGtByU2LZmYcDR7L+7eeWv+95Zee8U+ywLFenG1cAneJ+Qluo0SBQ0HLCqN96/cbuyXJYq/uXY8Fd621URwL/t4IHetG1vAZGT+/b3uXpL7XDvgHoc0SWZbwvoayQPXuKV55x/pv33zttddee+3lRw8cCpS8r+0yChGbxQltGGUadL6Gqgdf65NUXFm36e++OSsnJycnJ2fWgpKSRTMzDrQYWyV3t/TMvGnBpTaKk7OgZNHMRSu+GWNCLIkKXrLdeAu+x1k0ur167fZuKeZ7wqIFOXYab8ECPm6tawxIz8q6O6b4P5DMX5XZKE5Ye5PZDTxngdUuZfZLxBFCGHslAoARShscHEx1GzBM/rNji42TE00/b69eEzwLtdj6TDl7CV30GhXGL3AmcHLrjaVY6LTlyDjBoB5P8cKbVyzIkXToxccfDe4354yAgRwR8UxXlvvZe/iZ9k1fWVe7PDvQO1atDvujsmUfBdMEmuf/QazWRqY/w80dgdh/XzZm3gNrZeVdgWXSfO3Bs3s9nuKb71phlCdhbwp265/Eb9cK5PWsrKtdLmeMtGCsZEZVWHfaNRRHCADgVixi52DZy2vrVnqir7TOLqhIZsUaGwkt4tTdar3bS+ASUkfwL6njKY5YFse8znFtxfKC7Ozs7OzsguUVtY2mHW8et313BReij1gKvKDCvHiV5Fl5l60PDUOrF3dt39jg8y+UZH1Amx22s7Ad+yi4kFPXdmNHceNvJsaiXOHp7ZgnttBCkFG7ydmXaansru0b1ixdurSsbOnSpYG91Isr62prK/xvCdmBhc8D999oq42sQ2/XezbE+nDx72o4Y1q2Y0ZaYH3EOKlCTAv12TcURwgA4FYU8M6Wvdz623OnfUJnL79rpcfjWVlXW1EQ99vzGEsM+Xx2OsCVClZUVtbVVkRusbT8rpUej+VyOQUV1jvh2FRoIfqwHwc35DF0bX888ea7KWUuLYw5qThrEcfarcg2gke5XdvXVLf7F36KtyhXcO1mi03Z7My06PSjtipt4zLvTC1JXcGKw7OyzuJ9z/SdkfWa+ykTev21Z0P0Dtu+4EKEgdox5irhtmL+YiLGlmNhd6+otP8o5AjBYLcjBAAYIQp413LYJ3T28traYZ765muvXrNmzVJbBcwusDzMyF5eG+t8RWcc4wZYTMFbrXduubecrZiqckkJVgm3e8Vr/ophw8aWxA8IbsoW8U2M7QVnS+07/WmloKK2sa5y5cri4uLilSsrK43eirE3lkznGtitg8zno+zZsKasrLqhvb29vb29obqsLHBWgfnLsGBxnLJ3N19DWaIPwbCzJNYk/kAJfWN25r9gSSJOAnY7QkiQyGVHCAAwMhTwbma3T+jhi72NTOhyvj0bbF4rJuIvih0hcgrefElocWVjY2P8/eHtJKyEj71PkcHufWQ+s9eY4bXnVw0jZpotte/0p6XsguXLKyoqKpYvz3m3pUvxx1ywgrddNxqnZ/v/0dW1Z/uGDRs2bNiwPdbGXClO4mvYuL0r8YegeQv7JKbhQ5etnOEvWJKMk4iNjhBGmOisOEIAgBAKeJez0Sf0aRCxsliCk+ucIlERaQvmKfjw6t04xaCgotZUw2+09bgLlfCeGdMS3Nf+TH/xUoIp6sT7K9tWxAIGKW7NsAzlst24Z4akSPby2rrKlcXRX2l5ilfW2Wx1Qf83jkl8CIYtdrEmiVPpUyH5OAl/k02OEEYvkZk7jxAAgAL+bBDxCe3IL6EDh7rmw1j7rws+RFYhbcs/odb17osW1bshWMPbfTE7/8RbcWXCCyyd0UfmBfcUbybRv9JYnHO47Sx0QYNjS3hJ8c+/9r3YYpyNbtNvWIwlOOvqKitXFhcXFxevrKysq2uMWv3DLEV/OqFzZxKWiObFCrq2r4m+yD/aGe+fIcRJyB5HCCNLdFYcIQBACAX8WSH4Ce2aL6Hd99kcXAAuetFgW/LPmOzZbl29GwoqausqndE7BRWJ/zKc00fZy2tN8/Bd29eUVTeEH5eHVhqLs26fzYWWEXNkCR8oWWKfJOGQb1iyswsKlldUVFRULC8oyLZuaeCrCFv86SQsEc0nEHXt2RBrIj74hpDaL/RGsYa3yRHCyBO57wgBAMwo4M8W2QUVdXUu+Rhz+mezrz1qjsPXsHF71MJP9ma+HDzOZsLZNjgcHAaLPmqvdlQfZRdUNJquUt6zfcOapUvLyqqrq6vLysz7lznu78fEXMInseqYvZi2w9tQ1hB7LXdnjLe42qv9p+nYon6XkqvhTX8+29csjZyJ99npDWF0ang7HSGMJJHTjxAAIKGxP/rRj1LdBpwhEydOTHUThsvX9lhLtyTPopsu9jn7s9nXULbmwcZdj7X1ZEwZc9jn8/na/rPq+w8+3ydJxZXbb7801S1M0sRZczLadr3WF696d6r26r/ZsH3XY21tPZ9OGXPY52v7z6ot9ze+4sA+KrlpUUbPgde6+4wf9PV1d3d39/n/5SmuvL+iwLHvCpKknAUze4w3B3W39My8aYE9zzW3FPwTkvpea9llDLdJab43f/mfVVsedOB482uvXrr25z0ZUy6elHb48OE3f7m16sE9/iyblqeof958pq6lW1JxZd1fp+9q6ZbU193S1jOzJPaImTirZNHMngMtxl9PX3fLrscea2vr6ehoa/v5z+//WWN36jpoOHESSukRwsgSuegIAQCSkTY4OJjqNgCJtFcvNXbp9ni6AqscO/Kz2WqvtYDiSlucvTgkvoaGQ8uXu6t6j9NHnuLKuxzXR5Lka294/NGW4ArhHk/xwptXLHdiEivG24Mj3xCk9uoy/8kQ0Zw43mL++aT0/S3QKmOQhM3QJv7+0ddQvXG7ZR+lqoNGFMeWRpjINUcIAJAcZuDhBGk+Y6KqLzh36NDP5omz5szMOHooOCPq5ymuvH/T8kudNxc6cdYsB814JmfirDkzew4cCs5T+3mKK+//Ryf2kSRNzJm1oKTkpoCSkgWzcpyZxErOgpsWzVy04psOfEOQchaULLIYbx5P8R2OHG8TJ00JTloHeIpX3r/p9lRm8c/vepZ+e8WsiZqYs2DRzJ6kp3knzlpQctOimRlHD/0xdPaKp3jpHeu/l6IOGlEcWxphItccIQBAcpiBhyOETeu44bPZ52s/dEiScnJiLfuEFPO1t/t3d84pcNg0KBzIVe8J9vrjCUzQFleatrdz7sS1y+JoFBK57ggBAOJiBh6OMHHSp/6LRV3y2TxxYo7BuQsTuF6gi3JcNFkN+3LVe4K9/nj8E7TFf73ONJHr3Ilrl8XRKCRy3RECAMRFAQ9nmDhrSs9jLWl8NgMAhmLirJJFGW1HZ/9dRAHo0KLXZXE0Gok4QgBwVuEUegAAcJZy9Mnn0VwWR25MBAAjxAw8AAA4Szl34tqSy+LIjYkAYIQo4AEAwNnLZSWiy+LIjYkAYCQo4AEAwFnNZSWiy+LIjYkAYNgo4AEAwNkuokTsmblogS0W0R8ml8WRGxMBwPCMSXUDAAAAUi+7oKKusliSZ2VdReo3sB8pl8WRGxMBwDCwCj0AAICfz+fLznZPceiyOHJjIgAYEgp4AAAAAAAcgFPoAQAAAABwAAp4AAAAAAAcgAIeAAAAAAAHoIAHAAAAAMABKOABAAAAAHAACngAAAAAAByAAh4AAAAAAAeggAcAAAAAwAEo4AEAAAAAcAAKeAAAAAAAHIACHgAAAAAAB6CABwAAAADAASjgAQAAAABwAAp4AAAAAAAcgAIeAAAAAAAHoIAHAAAAAMABKOABAAAAAHAACngAAAAAAByAAh4AAAAAAAeggAcAAAAAwAEo4AHYUvPqtCQUFRWtXl3b7E3qF61uPmOtH10jiZD4sd7mZosX8Iy+bN7aojPZQ7YeEtbdccb5XyMbvkAAAJzdKOABOFhra2t9ffnivLSi1bU2qHqcxttcu7oob/G9b6W2FbW3lrdKKm3aUpLShqSaPbrDULKuplBS/WJKeAAAbIUCHoAbtNaX5xVRawyFt7Yob3F5fWvKm2GU74U1687q8t0m3RGUW/YwJTwAAPZDAQ/A5kqbBmPo7OxsqiktDNyx1bLWKNkSuLtj53ddECGWUPn+cFnumXpSF7+eoym37J5SiRIeAABboYAH4Fi5ubklZVv2DjaVBn5Sfy9n0jtJc1V5qySV3nPmynckzX8ePSU8AAD2QQEPwPFKtgRL+NbHd1HBO4W39t56SVLp9cyE21JgEp5vxgAAsAsKeAAukDcrcCJ964HOlLYESQtMv5/tV7/bWsn1RgXfWl7FJDwAADZAAQ/ABXIvmR3rphh7hgV/XOSfWvQ2164uKioK26Gu2TvEacfgb415yrFpe7yiGJOaUU1LuO2Z12i8qe3x9tbz/7q88sCCaa3leYm3VfN6myNen9W1Q359zL8vMP1euOJbFqfPB3aWCzbJeH7zDoJR3ZPsq3B6hkRSm9NZ3Gmo3RHZ2Un2RGSYZB8XOI1e9U9TwQMAkHoU8ABcpXBW3tAf1Ly6KC1vcXl9a2toEfDW1tb6xXl5Q9ufLjBfGbPaaX66PvQM1icLeN/ab/yPdWUbdffa1UV5RuNDv7m1vnxxXtHq0dpQ3Nu8uigvb3HE61Nfvjhv2E/i3fW48buSSRl6/tDP/N0T6B1v7eoiy1ch5vck8Y3akBhN3ubVRWmRnR3oidhBvVZhTI+L34W531rhr+A5jR4AgNSjgAfgAsG6OLmiN8zjt6Ytjr17V2t9ed4QVvAKnstvXcEHi/PY9wlWtpp9ScIozauL8mJuPdZavzhv9dOJfkVC++8tyov9ArXWL751GHVd4PT5ZDqseXXs528tz1vdLG9tvJehfMgNHNUhMVq8tfH6QcbMvUW74r18xuPqF8fNEzy9hQUmAABIPQp4AI7XvHqxv34fxmrmgVnJwtKmzs7gBnVNNYWhuwxh6jE4Xan9b0U/JlScx7xP6C6JV3ZrXm2qzCLbb2yvV19fH/Uw/y5qncGIhTWdcbZV879AhaU1TdavzzAq5NAXGQm/pahfbHSuOV/Y09ffW+Tfis7Uws5O8/6CQ7x+e3SHRCLJdUfz6tA59hENM++lWL84Yh4+eKWCpMIa08MGOzubQi9R3DzB80qo4AEASDkKeACO5TUuB04Llu9Nw93Xu7Spc++WktxgLZlbUmbenW4IhUuwgrd6TOeB8JnQ6PsMoX43l2YW7d+ytzMUYKRKmzr3bikrifn6DHHlwKF8SyFJKqwJy5dbUrbX9Oytrf57hFqYm1u2xXSXYVy/PXpDYhSY+rqwpnMwvGFGZwfq//BvK0KvdGFN594y08OUm1tieonifskRPK+EJSIBAEg1CngANle/OC2GPONyYEkqLK3ptJw/TkZp05YSi3ng4MTjkAqXOBV84ET/wpqawJRm5O8NlvgJK9vgSegqrOm0bH+uaXu9EUn8+lidbhBHMGVyKxYU1jwcfWJFcHG1mPcwN3CoRnVIjJi5r62CSsotezjwephn00NfGVmf6mB6FeP0YWiNSFayAwAgxSjgAbhB6/7Hq4a7ZFvMWnl4BWDMCj5w3njhim99a5b1ifbBa/kT1+/B1fDiXUUeVuUOVxKvz9Cq2SGcQC/FDGjaeSDWSxDaXXCI3zCM8pAYoVBfx7tEJLhnu3nghV6B+nst16rLLdvrP6V+b5yrT4b9QgIAgFFGAQ/AFVpb64e54viw1q2PJ7ToV1hZGzybefYluTHWBUu+fg/VwPFXgQtdkz9sSZ7lPgSRVxLEl8Qyd0l9DzCUpxztITEipr6O3y6LM91N33K01i/OS0srWj2c3f/ibNMIAADOKAp4ADZX2jQYU6exgpdpubK8Idfwo139xdpMLlC2ll5fEuOq4tBa+gkLyESnRgfZsPRKuiBNmdEfEiMR6uvQDvHWl5QEl7kLzWgkDdEAAAVTSURBVJNHXkbRWm/sHZeWlpbUNvCRuAoeAIDUooAH4GC5xgpe5vXahrrk+OlgVcEHL4CflSfzzHjoLkPcAD5JobOfcVYq2WJebt7Evw18WlGCjeABAICNUMADcIGw9dpssNBWsIIPToWaLoDPlcwz44G7BE+xH9X6Hcgt2bI3fLe5MMbVJynY2R4AAAwdBTwAdzAtL2ajCj5wjbv5AvjwewTucrrq96FdcA47i3c1SRiLBemMc1X8+8abLzvxi9pBHgAA2BEFPACcBhEVfNgF8IaIy+AD90iufk96WfDQBee2ETr54Ky7oHpYvTHKS8Dn5paUlW3Z66/lTZPySW1tb9dlCwAAOFtQwANwB1NtZIsiI1B2tT6+yxtxAbwh/DL44AJ2yS2gFnpwgrLLjhPwZ+9l+cPqDdMXHsmU2GbNqwML3FmeIZ+bW7Jlb2dwp8HY36fY8GsgAADOUhTwAFwheAa6bLKIeGg7+AOdzREXwPvvYboMvjbpDeQif33cVfu8tffWx7otdaKv/3eDJCbKQxu6D0no6pD4KzRGl+umbeBjXlaSzD4FSW96AAAATjMKeAAu4K29NbiF1mnYuHxYgiV2/b33Rl4AbwhdBl9ePsT6PWyH91iXLzevNr0qdmK5iZ7TmSbKLctsb/PqxcP8NsW8vsPiGEvGe2uLAr++sGadfxSZB8m91pe4m77jiTn4QhPwNvnbAgDg7EUBD8DJvF5v8+qiotAO2KbqJdVCc/CtURfAG6LOJR9KfZRb9nDo5OfyvKLVtaHKzuttXl2Utrg+Xvkedmb2md1HzGoTPecz9Wb94rQic3c0164uyotfvsftjrBNFhbnhe/g7jV+ffBPoPSe0BJ2uWX3hGbvIx5njBLT42IOvuAEvC0uTgEA4KxGAQ/A5uoXp8WWl5e3uL41VKeWNlkswJ0qpvlPyar6ibjDUOc3c8v2huq61vryxXmhl8VfvBfW1JTG/gXBxwYeeqYWInflSfSmYlmm1zQtLS1vcbnRHaVNTcPsjpItoWvVgzu4Bzu7PPhVTWFN55awQWSu/cMfFxolRtO2xBp8wTP/2eAQAICUo4AH4BKFpTWdgzGLkJQIu7zYqvoJv/54GPObJVsGTYVdhMLSpr1ll8R+7LqoR56xU9qDzz3kddnsrGRL2LLu4QpLmzrjDc9E3ZFbtjfObzeeoabT4vuruK0yHhj3L4f6HQAAG6GAB+BkhYWFhaWlNU2dnYN7t9hn7j3AdPGy9fJf5jsMsz7KLds72NnZFLa1d2FhaU1T594EX2f4S0JTbVeoMzUhHrq+wE0VvHJLtuwdtOyMzr1bSuJ3b+LuCP728G3cC41niP0XEHhcjeXjOq22jQ8J1u/mU/MBAECKpA0ODqa6DQCAs0/z6rTF9ZIKLeeNYQ+BXlJpk83ObwEA4KzEDDwAIBUC54zH3xsNqRRco94+a0MCAHB2o4AHAKREcNW3WFucIcW8u/wbIHL6PAAANkEBDwBIESbhba25qty/eD4nzwMAYBMU8ACAVAluZs8kvO0ETp/n7HkAAGyERewAAKnkrS3KK29llTR7CfQKSwwCAGArFPAAgNSihLcd/+Lz9AgAADZDAQ8AAAAAgANwDTwAAAAAAA5AAQ8AAAAAgANQwAMAAAAA4AAU8AAAAAAAOAAFPAAAAAAADkABDwAAAACAA1DAAwAAAADgABTwAAAAAAA4AAU8AAAAAAAOQAEPAAAAAIADUMADAAAAAOAAFPAAAAAAADgABTwAAAAAAA5AAQ8AAAAAgANQwAMAAAAA4AAU8AAAAAAAOAAFPAAAAAAADvD/A0p5NXPhJNKMAAAAAElFTkSuQmCC" width="672" /></p>
<table>
<caption>Equivalence bounds by bin width and k (cabinet approval, 1st
gold medal)</caption>
<colgroup>
<col width="17%" />
<col width="5%" />
<col width="13%" />
<col width="14%" />
<col width="16%" />
<col width="14%" />
<col width="16%" />
</colgroup>
<thead>
<tr class="header">
<th align="right">bin_minutes</th>
<th align="right">k</th>
<th align="left">category</th>
<th align="right">EB_TB_low</th>
<th align="right">EB_TB_high</th>
<th align="right">EB_CS_low</th>
<th align="right">EB_CS_high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.066</td>
<td align="right">0.066</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.035</td>
<td align="right">0.035</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.037</td>
<td align="right">0.037</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.029</td>
<td align="right">0.029</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.028</td>
<td align="right">0.028</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.022</td>
<td align="right">0.022</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.025</td>
<td align="right">0.025</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.019</td>
<td align="right">0.019</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.020</td>
<td align="right">0.020</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.013</td>
<td align="right">0.013</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.023</td>
<td align="right">0.023</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.015</td>
<td align="right">0.015</td>
<td align="right">-0.085</td>
<td align="right">0.085</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.3</td>
<td align="left">Both</td>
<td align="right">-0.098</td>
<td align="right">0.098</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.053</td>
<td align="right">0.053</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.055</td>
<td align="right">0.055</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.043</td>
<td align="right">0.043</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.042</td>
<td align="right">0.042</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.033</td>
<td align="right">0.033</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.038</td>
<td align="right">0.038</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.028</td>
<td align="right">0.028</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.030</td>
<td align="right">0.030</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.019</td>
<td align="right">0.019</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.035</td>
<td align="right">0.035</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.022</td>
<td align="right">0.022</td>
<td align="right">-0.128</td>
<td align="right">0.128</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-0.164</td>
<td align="right">0.164</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-0.088</td>
<td align="right">0.088</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-0.092</td>
<td align="right">0.092</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.072</td>
<td align="right">0.072</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.071</td>
<td align="right">0.071</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.056</td>
<td align="right">0.056</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.063</td>
<td align="right">0.063</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.047</td>
<td align="right">0.047</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.050</td>
<td align="right">0.050</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.031</td>
<td align="right">0.031</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.058</td>
<td align="right">0.058</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.036</td>
<td align="right">0.036</td>
<td align="right">-0.213</td>
<td align="right">0.213</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.328</td>
<td align="right">0.328</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.177</td>
<td align="right">0.177</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.183</td>
<td align="right">0.183</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.144</td>
<td align="right">0.144</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.141</td>
<td align="right">0.141</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.111</td>
<td align="right">0.111</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.094</td>
<td align="right">0.094</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.100</td>
<td align="right">0.100</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-0.063</td>
<td align="right">0.063</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.115</td>
<td align="right">0.115</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-0.073</td>
<td align="right">0.073</td>
<td align="right">-0.425</td>
<td align="right">0.425</td>
</tr>
</tbody>
</table>
</div>
<div id="figure-5-for-the-second-gold-medal-cross-sectional-equivalence-is-missed-at-k-0.2-but-holds-from-k-0.3-upward-and-time-bin-equivalence-appears-mainly-at-k-1.0-with-narrow-bins-implying-at-most-a-small-effect-on-the-feeling-thermometer" class="section level3 unnumbered">
<h3 class="unnumbered">Figure 5: For the second gold medal,
cross-sectional equivalence is missed at k = 0.2 but holds from k = 0.3
upward, and time-bin equivalence appears mainly at k = 1.0 with narrow
bins, implying at most a small effect on the feeling thermometer</h3>
<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a><span class="cf">if</span> (<span class="fu">exists</span>(<span class="st">&quot;df_rdd66&quot;</span>)) {</span>
<span id="cb11-2"><a href="#cb11-2" tabindex="-1"></a></span>
<span id="cb11-3"><a href="#cb11-3" tabindex="-1"></a>  cs_mode_use <span class="ot">&lt;-</span> <span class="st">&quot;pooled&quot;</span></span>
<span id="cb11-4"><a href="#cb11-4" tabindex="-1"></a>  bin_set_use <span class="ot">&lt;-</span> <span class="fu">seq</span>(<span class="dv">10</span>, <span class="dv">120</span>, <span class="at">by =</span> <span class="dv">10</span>)   </span>
<span id="cb11-5"><a href="#cb11-5" tabindex="-1"></a>  k_set_use   <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span>, <span class="fl">1.0</span>)</span>
<span id="cb11-6"><a href="#cb11-6" tabindex="-1"></a></span>
<span id="cb11-7"><a href="#cb11-7" tabindex="-1"></a>  tab_cabap_2nd <span class="ot">&lt;-</span> <span class="fu">build_overlap_singlecs</span>(</span>
<span id="cb11-8"><a href="#cb11-8" tabindex="-1"></a>    <span class="at">df        =</span> df_rdd66,</span>
<span id="cb11-9"><a href="#cb11-9" tabindex="-1"></a>    <span class="at">outcome   =</span> <span class="st">&quot;cabap&quot;</span>,</span>
<span id="cb11-10"><a href="#cb11-10" tabindex="-1"></a>    <span class="at">time_col  =</span> <span class="st">&quot;running_var&quot;</span>,  </span>
<span id="cb11-11"><a href="#cb11-11" tabindex="-1"></a>    <span class="at">bin_set   =</span> bin_set_use,</span>
<span id="cb11-12"><a href="#cb11-12" tabindex="-1"></a>    <span class="at">k_set     =</span> k_set_use,</span>
<span id="cb11-13"><a href="#cb11-13" tabindex="-1"></a>    <span class="at">cs_mode   =</span> cs_mode_use,</span>
<span id="cb11-14"><a href="#cb11-14" tabindex="-1"></a>    <span class="at">unit_time =</span> <span class="st">&quot;mins&quot;</span></span>
<span id="cb11-15"><a href="#cb11-15" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
<span id="cb11-16"><a href="#cb11-16" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb11-17"><a href="#cb11-17" tabindex="-1"></a>    <span class="at">EB_TB_low  =</span> <span class="sc">-</span>eps_tb,</span>
<span id="cb11-18"><a href="#cb11-18" tabindex="-1"></a>      <span class="at">EB_TB_high =</span>  eps_tb,</span>
<span id="cb11-19"><a href="#cb11-19" tabindex="-1"></a>      <span class="at">EB_CS_low  =</span> <span class="sc">-</span>eps_cs,</span>
<span id="cb11-20"><a href="#cb11-20" tabindex="-1"></a>      <span class="at">EB_CS_high =</span>  eps_cs</span>
<span id="cb11-21"><a href="#cb11-21" tabindex="-1"></a>    )</span>
<span id="cb11-22"><a href="#cb11-22" tabindex="-1"></a></span>
<span id="cb11-23"><a href="#cb11-23" tabindex="-1"></a>  p_cabap_2nd <span class="ot">&lt;-</span> <span class="fu">plot_overlap_blue_EB</span>(</span>
<span id="cb11-24"><a href="#cb11-24" tabindex="-1"></a>    tab_cabap_2nd,</span>
<span id="cb11-25"><a href="#cb11-25" tabindex="-1"></a>    <span class="fu">common_ci90</span>(df_rdd66, <span class="st">&quot;cabap&quot;</span>),</span>
<span id="cb11-26"><a href="#cb11-26" tabindex="-1"></a>    <span class="at">title   =</span> <span class="st">&quot;Cabinet approval: overlap of equivalence (2nd gold medal)&quot;</span>,</span>
<span id="cb11-27"><a href="#cb11-27" tabindex="-1"></a>    <span class="at">cs_mode =</span> cs_mode_use</span>
<span id="cb11-28"><a href="#cb11-28" tabindex="-1"></a>  )</span>
<span id="cb11-29"><a href="#cb11-29" tabindex="-1"></a></span>
<span id="cb11-30"><a href="#cb11-30" tabindex="-1"></a>  <span class="fu">print</span>(p_cabap_2nd)</span>
<span id="cb11-31"><a href="#cb11-31" tabindex="-1"></a></span>
<span id="cb11-32"><a href="#cb11-32" tabindex="-1"></a>  tab_bounds_cabap_2nd <span class="ot">&lt;-</span> tab_cabap_2nd <span class="sc">%&gt;%</span></span>
<span id="cb11-33"><a href="#cb11-33" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">select</span>(bin_minutes, k, category,</span>
<span id="cb11-34"><a href="#cb11-34" tabindex="-1"></a>                  EB_TB_low, EB_TB_high, EB_CS_low, EB_CS_high) <span class="sc">%&gt;%</span></span>
<span id="cb11-35"><a href="#cb11-35" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">arrange</span>(k, bin_minutes)</span>
<span id="cb11-36"><a href="#cb11-36" tabindex="-1"></a></span>
<span id="cb11-37"><a href="#cb11-37" tabindex="-1"></a>  knitr<span class="sc">::</span><span class="fu">kable</span>(</span>
<span id="cb11-38"><a href="#cb11-38" tabindex="-1"></a>    tab_bounds_cabap_2nd,</span>
<span id="cb11-39"><a href="#cb11-39" tabindex="-1"></a>    <span class="at">digits  =</span> <span class="dv">3</span>,</span>
<span id="cb11-40"><a href="#cb11-40" tabindex="-1"></a>    <span class="at">caption =</span> <span class="st">&quot;Equivalence bounds by bin width and k (cabinet approval, 2nd gold medal)&quot;</span></span>
<span id="cb11-41"><a href="#cb11-41" tabindex="-1"></a>  )</span>
<span id="cb11-42"><a href="#cb11-42" tabindex="-1"></a>}</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
<table>
<caption>Equivalence bounds by bin width and k (cabinet approval, 2nd
gold medal)</caption>
<colgroup>
<col width="17%" />
<col width="5%" />
<col width="13%" />
<col width="14%" />
<col width="16%" />
<col width="14%" />
<col width="16%" />
</colgroup>
<thead>
<tr class="header">
<th align="right">bin_minutes</th>
<th align="right">k</th>
<th align="left">category</th>
<th align="right">EB_TB_low</th>
<th align="right">EB_TB_high</th>
<th align="right">EB_CS_low</th>
<th align="right">EB_CS_high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.050</td>
<td align="right">0.050</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.038</td>
<td align="right">0.038</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.032</td>
<td align="right">0.032</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.027</td>
<td align="right">0.027</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.028</td>
<td align="right">0.028</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.022</td>
<td align="right">0.022</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.021</td>
<td align="right">0.021</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.020</td>
<td align="right">0.020</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.011</td>
<td align="right">0.011</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.018</td>
<td align="right">0.018</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.019</td>
<td align="right">0.019</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.011</td>
<td align="right">0.011</td>
<td align="right">-0.084</td>
<td align="right">0.084</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.076</td>
<td align="right">0.076</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.058</td>
<td align="right">0.058</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.047</td>
<td align="right">0.047</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.040</td>
<td align="right">0.040</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.042</td>
<td align="right">0.042</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.034</td>
<td align="right">0.034</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.032</td>
<td align="right">0.032</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.030</td>
<td align="right">0.030</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.016</td>
<td align="right">0.016</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.027</td>
<td align="right">0.027</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.029</td>
<td align="right">0.029</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.016</td>
<td align="right">0.016</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-0.126</td>
<td align="right">0.126</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.096</td>
<td align="right">0.096</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.079</td>
<td align="right">0.079</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.067</td>
<td align="right">0.067</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.070</td>
<td align="right">0.070</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.056</td>
<td align="right">0.056</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.053</td>
<td align="right">0.053</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.050</td>
<td align="right">0.050</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.026</td>
<td align="right">0.026</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.044</td>
<td align="right">0.044</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.048</td>
<td align="right">0.048</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.027</td>
<td align="right">0.027</td>
<td align="right">-0.210</td>
<td align="right">0.210</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.252</td>
<td align="right">0.252</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.192</td>
<td align="right">0.192</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.158</td>
<td align="right">0.158</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.134</td>
<td align="right">0.134</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.139</td>
<td align="right">0.139</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-0.112</td>
<td align="right">0.112</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-0.106</td>
<td align="right">0.106</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-0.099</td>
<td align="right">0.099</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-0.053</td>
<td align="right">0.053</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-0.089</td>
<td align="right">0.089</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-0.095</td>
<td align="right">0.095</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-0.053</td>
<td align="right">0.053</td>
<td align="right">-0.419</td>
<td align="right">0.419</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="specification-curve" class="section level2 unnumbered">
<h2 class="unnumbered">Specification curve</h2>
<div id="figure-6-specification-curve-shows-the-statistical-insignificance-of-the-cutoff-the-first-gold-medal-in-all-covariates-combinations" class="section level3 unnumbered">
<h3 class="unnumbered">Figure 6: Specification curve shows the
statistical insignificance of the cutoff (the first gold medal) in all
covariates combinations</h3>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1" tabindex="-1"></a>variables <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;age&quot;</span>, <span class="st">&quot;female&quot;</span>, <span class="st">&quot;income&quot;</span>, <span class="st">&quot;education&quot;</span>, <span class="st">&quot;psu_rul&quot;</span>, <span class="st">&quot;patriotism&quot;</span>)</span>
<span id="cb12-2"><a href="#cb12-2" tabindex="-1"></a></span>
<span id="cb12-3"><a href="#cb12-3" tabindex="-1"></a>specifications <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(variables),</span>
<span id="cb12-4"><a href="#cb12-4" tabindex="-1"></a>                         <span class="cf">function</span>(n) <span class="fu">combn</span>(variables, n, <span class="at">simplify =</span> <span class="cn">FALSE</span>))</span>
<span id="cb12-5"><a href="#cb12-5" tabindex="-1"></a>specifications <span class="ot">&lt;-</span> <span class="fu">unlist</span>(specifications, <span class="at">recursive =</span> <span class="cn">FALSE</span>)</span>
<span id="cb12-6"><a href="#cb12-6" tabindex="-1"></a></span>
<span id="cb12-7"><a href="#cb12-7" tabindex="-1"></a>res_list <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="fu">seq_along</span>(specifications), <span class="cf">function</span>(i) {</span>
<span id="cb12-8"><a href="#cb12-8" tabindex="-1"></a>  form <span class="ot">&lt;-</span> <span class="fu">as.formula</span>(</span>
<span id="cb12-9"><a href="#cb12-9" tabindex="-1"></a>    <span class="fu">paste</span>(<span class="st">&quot;cabap ~ cutoff +&quot;</span>, <span class="fu">paste</span>(specifications[[i]], <span class="at">collapse =</span> <span class="st">&quot; + &quot;</span>))</span>
<span id="cb12-10"><a href="#cb12-10" tabindex="-1"></a>  )</span>
<span id="cb12-11"><a href="#cb12-11" tabindex="-1"></a>  m <span class="ot">&lt;-</span> <span class="fu">lm</span>(form, <span class="at">data =</span> df_rdd48)</span>
<span id="cb12-12"><a href="#cb12-12" tabindex="-1"></a>  ci <span class="ot">&lt;-</span> <span class="fu">confint</span>(m)[<span class="st">&quot;cutoff&quot;</span>, ]</span>
<span id="cb12-13"><a href="#cb12-13" tabindex="-1"></a>  <span class="fu">data.frame</span>(</span>
<span id="cb12-14"><a href="#cb12-14" tabindex="-1"></a>    <span class="at">SpecID      =</span> i,                               </span>
<span id="cb12-15"><a href="#cb12-15" tabindex="-1"></a>    <span class="at">Coefficient =</span> <span class="fu">coef</span>(m)[<span class="st">&quot;cutoff&quot;</span>],</span>
<span id="cb12-16"><a href="#cb12-16" tabindex="-1"></a>    <span class="at">ConfLow     =</span> ci[<span class="dv">1</span>],</span>
<span id="cb12-17"><a href="#cb12-17" tabindex="-1"></a>    <span class="at">ConfHigh    =</span> ci[<span class="dv">2</span>]</span>
<span id="cb12-18"><a href="#cb12-18" tabindex="-1"></a>  )</span>
<span id="cb12-19"><a href="#cb12-19" tabindex="-1"></a>})</span>
<span id="cb12-20"><a href="#cb12-20" tabindex="-1"></a></span>
<span id="cb12-21"><a href="#cb12-21" tabindex="-1"></a>results <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(res_list)</span>
<span id="cb12-22"><a href="#cb12-22" tabindex="-1"></a></span>
<span id="cb12-23"><a href="#cb12-23" tabindex="-1"></a>results <span class="ot">&lt;-</span> results <span class="sc">%&gt;%</span></span>
<span id="cb12-24"><a href="#cb12-24" tabindex="-1"></a>  <span class="fu">arrange</span>(Coefficient) <span class="sc">%&gt;%</span></span>
<span id="cb12-25"><a href="#cb12-25" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Specification =</span> <span class="fu">row_number</span>())</span>
<span id="cb12-26"><a href="#cb12-26" tabindex="-1"></a></span>
<span id="cb12-27"><a href="#cb12-27" tabindex="-1"></a>spec_order <span class="ot">&lt;-</span> results <span class="sc">%&gt;%</span> <span class="fu">select</span>(SpecID, Specification)</span>
<span id="cb12-28"><a href="#cb12-28" tabindex="-1"></a></span>
<span id="cb12-29"><a href="#cb12-29" tabindex="-1"></a>variables_long <span class="ot">&lt;-</span> <span class="fu">do.call</span>(</span>
<span id="cb12-30"><a href="#cb12-30" tabindex="-1"></a>  rbind,</span>
<span id="cb12-31"><a href="#cb12-31" tabindex="-1"></a>  <span class="fu">lapply</span>(<span class="fu">seq_along</span>(specifications), <span class="cf">function</span>(i) {</span>
<span id="cb12-32"><a href="#cb12-32" tabindex="-1"></a>    <span class="fu">data.frame</span>(</span>
<span id="cb12-33"><a href="#cb12-33" tabindex="-1"></a>      <span class="at">SpecID   =</span> i,</span>
<span id="cb12-34"><a href="#cb12-34" tabindex="-1"></a>      <span class="at">Variable =</span> variables,</span>
<span id="cb12-35"><a href="#cb12-35" tabindex="-1"></a>      <span class="at">Included =</span> variables <span class="sc">%in%</span> specifications[[i]]</span>
<span id="cb12-36"><a href="#cb12-36" tabindex="-1"></a>    )</span>
<span id="cb12-37"><a href="#cb12-37" tabindex="-1"></a>  })</span>
<span id="cb12-38"><a href="#cb12-38" tabindex="-1"></a>)</span>
<span id="cb12-39"><a href="#cb12-39" tabindex="-1"></a></span>
<span id="cb12-40"><a href="#cb12-40" tabindex="-1"></a>variables_long <span class="ot">&lt;-</span> variables_long <span class="sc">%&gt;%</span></span>
<span id="cb12-41"><a href="#cb12-41" tabindex="-1"></a>  <span class="fu">left_join</span>(spec_order, <span class="at">by =</span> <span class="st">&quot;SpecID&quot;</span>) <span class="sc">%&gt;%</span>  </span>
<span id="cb12-42"><a href="#cb12-42" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb12-43"><a href="#cb12-43" tabindex="-1"></a>    <span class="at">Variable =</span> <span class="fu">factor</span>(Variable, <span class="at">levels =</span> <span class="fu">rev</span>(variables)),</span>
<span id="cb12-44"><a href="#cb12-44" tabindex="-1"></a>    <span class="at">Included =</span> <span class="fu">factor</span>(Included, <span class="at">levels =</span> <span class="fu">c</span>(<span class="cn">FALSE</span>, <span class="cn">TRUE</span>))</span>
<span id="cb12-45"><a href="#cb12-45" tabindex="-1"></a>  )</span>
<span id="cb12-46"><a href="#cb12-46" tabindex="-1"></a></span>
<span id="cb12-47"><a href="#cb12-47" tabindex="-1"></a>upper_panel <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(results,</span>
<span id="cb12-48"><a href="#cb12-48" tabindex="-1"></a>                      <span class="fu">aes</span>(<span class="at">x =</span> Specification, <span class="at">y =</span> Coefficient)) <span class="sc">+</span></span>
<span id="cb12-49"><a href="#cb12-49" tabindex="-1"></a>  <span class="fu">geom_point</span>() <span class="sc">+</span></span>
<span id="cb12-50"><a href="#cb12-50" tabindex="-1"></a>  <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> ConfLow, <span class="at">ymax =</span> ConfHigh), <span class="at">width =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb12-51"><a href="#cb12-51" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="at">color =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb12-52"><a href="#cb12-52" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb12-53"><a href="#cb12-53" tabindex="-1"></a>    <span class="at">title =</span> <span class="st">&quot;Sensitivity Analysis of Coefficient Estimates </span><span class="sc">\n</span><span class="st"> (Cutoff: 1st gold medal)&quot;</span>,</span>
<span id="cb12-54"><a href="#cb12-54" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>,</span>
<span id="cb12-55"><a href="#cb12-55" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Coefficient Estimate for &#39;cutoff&#39;&quot;</span></span>
<span id="cb12-56"><a href="#cb12-56" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb12-57"><a href="#cb12-57" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb12-58"><a href="#cb12-58" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb12-59"><a href="#cb12-59" tabindex="-1"></a>    <span class="at">axis.text.x  =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb12-60"><a href="#cb12-60" tabindex="-1"></a>    <span class="at">axis.ticks.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb12-61"><a href="#cb12-61" tabindex="-1"></a>    <span class="at">plot.margin  =</span> <span class="fu">margin</span>(<span class="at">t =</span> <span class="fl">5.5</span>, <span class="at">r =</span> <span class="fl">5.5</span>, <span class="at">b =</span> <span class="dv">0</span>, <span class="at">l =</span> <span class="dv">30</span>)</span>
<span id="cb12-62"><a href="#cb12-62" tabindex="-1"></a>  )</span>
<span id="cb12-63"><a href="#cb12-63" tabindex="-1"></a></span>
<span id="cb12-64"><a href="#cb12-64" tabindex="-1"></a>lower_panel <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(variables_long,</span>
<span id="cb12-65"><a href="#cb12-65" tabindex="-1"></a>                      <span class="fu">aes</span>(<span class="at">x =</span> Specification, <span class="at">y =</span> Variable, <span class="at">fill =</span> Included)) <span class="sc">+</span></span>
<span id="cb12-66"><a href="#cb12-66" tabindex="-1"></a>  <span class="fu">geom_tile</span>() <span class="sc">+</span></span>
<span id="cb12-67"><a href="#cb12-67" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;FALSE&quot;</span> <span class="ot">=</span> <span class="st">&quot;white&quot;</span>, <span class="st">&quot;TRUE&quot;</span> <span class="ot">=</span> <span class="st">&quot;gray&quot;</span>), <span class="at">guide =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb12-68"><a href="#cb12-68" tabindex="-1"></a>  <span class="fu">scale_y_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(</span>
<span id="cb12-69"><a href="#cb12-69" tabindex="-1"></a>    <span class="st">&quot;age&quot;</span>        <span class="ot">=</span> <span class="st">&quot;Age&quot;</span>,</span>
<span id="cb12-70"><a href="#cb12-70" tabindex="-1"></a>    <span class="st">&quot;female&quot;</span>     <span class="ot">=</span> <span class="st">&quot;Female&quot;</span>,</span>
<span id="cb12-71"><a href="#cb12-71" tabindex="-1"></a>    <span class="st">&quot;income&quot;</span>     <span class="ot">=</span> <span class="st">&quot;Income&quot;</span>,</span>
<span id="cb12-72"><a href="#cb12-72" tabindex="-1"></a>    <span class="st">&quot;education&quot;</span>  <span class="ot">=</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb12-73"><a href="#cb12-73" tabindex="-1"></a>    <span class="st">&quot;psu_rul&quot;</span>    <span class="ot">=</span> <span class="st">&quot;Ruling party support&quot;</span>,</span>
<span id="cb12-74"><a href="#cb12-74" tabindex="-1"></a>    <span class="st">&quot;patriotism&quot;</span> <span class="ot">=</span> <span class="st">&quot;Patriotism&quot;</span></span>
<span id="cb12-75"><a href="#cb12-75" tabindex="-1"></a>  )) <span class="sc">+</span></span>
<span id="cb12-76"><a href="#cb12-76" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb12-77"><a href="#cb12-77" tabindex="-1"></a>    <span class="at">x =</span> <span class="st">&quot;Specification Number&quot;</span>,</span>
<span id="cb12-78"><a href="#cb12-78" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Variables&quot;</span></span>
<span id="cb12-79"><a href="#cb12-79" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb12-80"><a href="#cb12-80" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb12-81"><a href="#cb12-81" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb12-82"><a href="#cb12-82" tabindex="-1"></a>    <span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">90</span>, <span class="at">vjust =</span> <span class="fl">0.5</span>, <span class="at">hjust =</span> <span class="dv">1</span>),</span>
<span id="cb12-83"><a href="#cb12-83" tabindex="-1"></a>    <span class="at">plot.margin =</span> <span class="fu">margin</span>(<span class="at">t =</span> <span class="dv">0</span>, <span class="at">r =</span> <span class="fl">5.5</span>, <span class="at">b =</span> <span class="fl">5.5</span>, <span class="at">l =</span> <span class="dv">30</span>)</span>
<span id="cb12-84"><a href="#cb12-84" tabindex="-1"></a>  )</span>
<span id="cb12-85"><a href="#cb12-85" tabindex="-1"></a></span>
<span id="cb12-86"><a href="#cb12-86" tabindex="-1"></a>g1 <span class="ot">&lt;-</span> <span class="fu">ggplotGrob</span>(upper_panel)</span>
<span id="cb12-87"><a href="#cb12-87" tabindex="-1"></a>g2 <span class="ot">&lt;-</span> <span class="fu">ggplotGrob</span>(lower_panel)</span>
<span id="cb12-88"><a href="#cb12-88" tabindex="-1"></a></span>
<span id="cb12-89"><a href="#cb12-89" tabindex="-1"></a>gw <span class="ot">&lt;-</span> grid<span class="sc">::</span><span class="fu">unit.pmax</span>(g1<span class="sc">$</span>widths, g2<span class="sc">$</span>widths)</span>
<span id="cb12-90"><a href="#cb12-90" tabindex="-1"></a>g1<span class="sc">$</span>widths <span class="ot">&lt;-</span> gw</span>
<span id="cb12-91"><a href="#cb12-91" tabindex="-1"></a>g2<span class="sc">$</span>widths <span class="ot">&lt;-</span> gw</span>
<span id="cb12-92"><a href="#cb12-92" tabindex="-1"></a></span>
<span id="cb12-93"><a href="#cb12-93" tabindex="-1"></a><span class="fu">grid.arrange</span>(g1, g2, <span class="at">ncol =</span> <span class="dv">1</span>, <span class="at">heights =</span> <span class="fu">c</span>(<span class="dv">2</span>, <span class="dv">1</span>))</span></code></pre></div>
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" width="672" /></p>
</div>
<div id="figure-7-specification-curve-shows-the-statistical-insignificance-of-the-cutoff-the-second-gold-medal-in-all-covariates-combinations" class="section level3 unnumbered">
<h3 class="unnumbered">Figure 7: Specification curve shows the
statistical insignificance of the cutoff (the second gold medal) in all
covariates combinations</h3>
<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb13-1"><a href="#cb13-1" tabindex="-1"></a>variables <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;age&quot;</span>, <span class="st">&quot;female&quot;</span>, <span class="st">&quot;income&quot;</span>, <span class="st">&quot;education&quot;</span>, <span class="st">&quot;psu_rul&quot;</span>, <span class="st">&quot;patriotism&quot;</span>)</span>
<span id="cb13-2"><a href="#cb13-2" tabindex="-1"></a></span>
<span id="cb13-3"><a href="#cb13-3" tabindex="-1"></a>specifications <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(variables),</span>
<span id="cb13-4"><a href="#cb13-4" tabindex="-1"></a>                         <span class="cf">function</span>(n) <span class="fu">combn</span>(variables, n, <span class="at">simplify =</span> <span class="cn">FALSE</span>))</span>
<span id="cb13-5"><a href="#cb13-5" tabindex="-1"></a>specifications <span class="ot">&lt;-</span> <span class="fu">unlist</span>(specifications, <span class="at">recursive =</span> <span class="cn">FALSE</span>)</span>
<span id="cb13-6"><a href="#cb13-6" tabindex="-1"></a></span>
<span id="cb13-7"><a href="#cb13-7" tabindex="-1"></a>res_list <span class="ot">&lt;-</span> <span class="fu">lapply</span>(<span class="fu">seq_along</span>(specifications), <span class="cf">function</span>(i) {</span>
<span id="cb13-8"><a href="#cb13-8" tabindex="-1"></a>  form <span class="ot">&lt;-</span> <span class="fu">as.formula</span>(</span>
<span id="cb13-9"><a href="#cb13-9" tabindex="-1"></a>    <span class="fu">paste</span>(<span class="st">&quot;cabap ~ cutoff +&quot;</span>, <span class="fu">paste</span>(specifications[[i]], <span class="at">collapse =</span> <span class="st">&quot; + &quot;</span>))</span>
<span id="cb13-10"><a href="#cb13-10" tabindex="-1"></a>  )</span>
<span id="cb13-11"><a href="#cb13-11" tabindex="-1"></a>  m <span class="ot">&lt;-</span> <span class="fu">lm</span>(form, <span class="at">data =</span> df_rdd66)</span>
<span id="cb13-12"><a href="#cb13-12" tabindex="-1"></a>  ci <span class="ot">&lt;-</span> <span class="fu">confint</span>(m)[<span class="st">&quot;cutoff&quot;</span>, ]</span>
<span id="cb13-13"><a href="#cb13-13" tabindex="-1"></a>  <span class="fu">data.frame</span>(</span>
<span id="cb13-14"><a href="#cb13-14" tabindex="-1"></a>    <span class="at">SpecID      =</span> i,                               </span>
<span id="cb13-15"><a href="#cb13-15" tabindex="-1"></a>    <span class="at">Coefficient =</span> <span class="fu">coef</span>(m)[<span class="st">&quot;cutoff&quot;</span>],</span>
<span id="cb13-16"><a href="#cb13-16" tabindex="-1"></a>    <span class="at">ConfLow     =</span> ci[<span class="dv">1</span>],</span>
<span id="cb13-17"><a href="#cb13-17" tabindex="-1"></a>    <span class="at">ConfHigh    =</span> ci[<span class="dv">2</span>]</span>
<span id="cb13-18"><a href="#cb13-18" tabindex="-1"></a>  )</span>
<span id="cb13-19"><a href="#cb13-19" tabindex="-1"></a>})</span>
<span id="cb13-20"><a href="#cb13-20" tabindex="-1"></a></span>
<span id="cb13-21"><a href="#cb13-21" tabindex="-1"></a>results <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(res_list)</span>
<span id="cb13-22"><a href="#cb13-22" tabindex="-1"></a></span>
<span id="cb13-23"><a href="#cb13-23" tabindex="-1"></a>results <span class="ot">&lt;-</span> results <span class="sc">%&gt;%</span></span>
<span id="cb13-24"><a href="#cb13-24" tabindex="-1"></a>  <span class="fu">arrange</span>(Coefficient) <span class="sc">%&gt;%</span></span>
<span id="cb13-25"><a href="#cb13-25" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">Specification =</span> <span class="fu">row_number</span>())</span>
<span id="cb13-26"><a href="#cb13-26" tabindex="-1"></a></span>
<span id="cb13-27"><a href="#cb13-27" tabindex="-1"></a>spec_order <span class="ot">&lt;-</span> results <span class="sc">%&gt;%</span> <span class="fu">select</span>(SpecID, Specification)</span>
<span id="cb13-28"><a href="#cb13-28" tabindex="-1"></a></span>
<span id="cb13-29"><a href="#cb13-29" tabindex="-1"></a>variables_long <span class="ot">&lt;-</span> <span class="fu">do.call</span>(</span>
<span id="cb13-30"><a href="#cb13-30" tabindex="-1"></a>  rbind,</span>
<span id="cb13-31"><a href="#cb13-31" tabindex="-1"></a>  <span class="fu">lapply</span>(<span class="fu">seq_along</span>(specifications), <span class="cf">function</span>(i) {</span>
<span id="cb13-32"><a href="#cb13-32" tabindex="-1"></a>    <span class="fu">data.frame</span>(</span>
<span id="cb13-33"><a href="#cb13-33" tabindex="-1"></a>      <span class="at">SpecID   =</span> i,</span>
<span id="cb13-34"><a href="#cb13-34" tabindex="-1"></a>      <span class="at">Variable =</span> variables,</span>
<span id="cb13-35"><a href="#cb13-35" tabindex="-1"></a>      <span class="at">Included =</span> variables <span class="sc">%in%</span> specifications[[i]]</span>
<span id="cb13-36"><a href="#cb13-36" tabindex="-1"></a>    )</span>
<span id="cb13-37"><a href="#cb13-37" tabindex="-1"></a>  })</span>
<span id="cb13-38"><a href="#cb13-38" tabindex="-1"></a>)</span>
<span id="cb13-39"><a href="#cb13-39" tabindex="-1"></a></span>
<span id="cb13-40"><a href="#cb13-40" tabindex="-1"></a>variables_long <span class="ot">&lt;-</span> variables_long <span class="sc">%&gt;%</span></span>
<span id="cb13-41"><a href="#cb13-41" tabindex="-1"></a>  <span class="fu">left_join</span>(spec_order, <span class="at">by =</span> <span class="st">&quot;SpecID&quot;</span>) <span class="sc">%&gt;%</span>  </span>
<span id="cb13-42"><a href="#cb13-42" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb13-43"><a href="#cb13-43" tabindex="-1"></a>    <span class="at">Variable =</span> <span class="fu">factor</span>(Variable, <span class="at">levels =</span> <span class="fu">rev</span>(variables)),</span>
<span id="cb13-44"><a href="#cb13-44" tabindex="-1"></a>    <span class="at">Included =</span> <span class="fu">factor</span>(Included, <span class="at">levels =</span> <span class="fu">c</span>(<span class="cn">FALSE</span>, <span class="cn">TRUE</span>))</span>
<span id="cb13-45"><a href="#cb13-45" tabindex="-1"></a>  )</span>
<span id="cb13-46"><a href="#cb13-46" tabindex="-1"></a></span>
<span id="cb13-47"><a href="#cb13-47" tabindex="-1"></a>upper_panel <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(results,</span>
<span id="cb13-48"><a href="#cb13-48" tabindex="-1"></a>                      <span class="fu">aes</span>(<span class="at">x =</span> Specification, <span class="at">y =</span> Coefficient)) <span class="sc">+</span></span>
<span id="cb13-49"><a href="#cb13-49" tabindex="-1"></a>  <span class="fu">geom_point</span>() <span class="sc">+</span></span>
<span id="cb13-50"><a href="#cb13-50" tabindex="-1"></a>  <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> ConfLow, <span class="at">ymax =</span> ConfHigh), <span class="at">width =</span> <span class="fl">0.2</span>) <span class="sc">+</span></span>
<span id="cb13-51"><a href="#cb13-51" tabindex="-1"></a>  <span class="fu">geom_hline</span>(<span class="at">yintercept =</span> <span class="dv">0</span>, <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="at">color =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb13-52"><a href="#cb13-52" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb13-53"><a href="#cb13-53" tabindex="-1"></a>    <span class="at">title =</span> <span class="st">&quot;Sensitivity Analysis of Coefficient Estimates </span><span class="sc">\n</span><span class="st"> (Cutoff: 2nd gold medal)&quot;</span>,</span>
<span id="cb13-54"><a href="#cb13-54" tabindex="-1"></a>    <span class="at">x =</span> <span class="cn">NULL</span>,</span>
<span id="cb13-55"><a href="#cb13-55" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Coefficient Estimate for &#39;cutoff&#39;&quot;</span></span>
<span id="cb13-56"><a href="#cb13-56" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb13-57"><a href="#cb13-57" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb13-58"><a href="#cb13-58" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb13-59"><a href="#cb13-59" tabindex="-1"></a>    <span class="at">axis.text.x  =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb13-60"><a href="#cb13-60" tabindex="-1"></a>    <span class="at">axis.ticks.x =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb13-61"><a href="#cb13-61" tabindex="-1"></a>    <span class="at">plot.margin  =</span> <span class="fu">margin</span>(<span class="at">t =</span> <span class="fl">5.5</span>, <span class="at">r =</span> <span class="fl">5.5</span>, <span class="at">b =</span> <span class="dv">0</span>, <span class="at">l =</span> <span class="dv">30</span>)</span>
<span id="cb13-62"><a href="#cb13-62" tabindex="-1"></a>  )</span>
<span id="cb13-63"><a href="#cb13-63" tabindex="-1"></a></span>
<span id="cb13-64"><a href="#cb13-64" tabindex="-1"></a>lower_panel <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(variables_long,</span>
<span id="cb13-65"><a href="#cb13-65" tabindex="-1"></a>                      <span class="fu">aes</span>(<span class="at">x =</span> Specification, <span class="at">y =</span> Variable, <span class="at">fill =</span> Included)) <span class="sc">+</span></span>
<span id="cb13-66"><a href="#cb13-66" tabindex="-1"></a>  <span class="fu">geom_tile</span>() <span class="sc">+</span></span>
<span id="cb13-67"><a href="#cb13-67" tabindex="-1"></a>  <span class="fu">scale_fill_manual</span>(<span class="at">values =</span> <span class="fu">c</span>(<span class="st">&quot;FALSE&quot;</span> <span class="ot">=</span> <span class="st">&quot;white&quot;</span>, <span class="st">&quot;TRUE&quot;</span> <span class="ot">=</span> <span class="st">&quot;gray&quot;</span>), <span class="at">guide =</span> <span class="st">&quot;none&quot;</span>) <span class="sc">+</span></span>
<span id="cb13-68"><a href="#cb13-68" tabindex="-1"></a>  <span class="fu">scale_y_discrete</span>(<span class="at">labels =</span> <span class="fu">c</span>(</span>
<span id="cb13-69"><a href="#cb13-69" tabindex="-1"></a>    <span class="st">&quot;age&quot;</span>        <span class="ot">=</span> <span class="st">&quot;Age&quot;</span>,</span>
<span id="cb13-70"><a href="#cb13-70" tabindex="-1"></a>    <span class="st">&quot;female&quot;</span>     <span class="ot">=</span> <span class="st">&quot;Female&quot;</span>,</span>
<span id="cb13-71"><a href="#cb13-71" tabindex="-1"></a>    <span class="st">&quot;income&quot;</span>     <span class="ot">=</span> <span class="st">&quot;Income&quot;</span>,</span>
<span id="cb13-72"><a href="#cb13-72" tabindex="-1"></a>    <span class="st">&quot;education&quot;</span>  <span class="ot">=</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb13-73"><a href="#cb13-73" tabindex="-1"></a>    <span class="st">&quot;psu_rul&quot;</span>    <span class="ot">=</span> <span class="st">&quot;Ruling party support&quot;</span>,</span>
<span id="cb13-74"><a href="#cb13-74" tabindex="-1"></a>    <span class="st">&quot;patriotism&quot;</span> <span class="ot">=</span> <span class="st">&quot;Patriotism&quot;</span></span>
<span id="cb13-75"><a href="#cb13-75" tabindex="-1"></a>  )) <span class="sc">+</span></span>
<span id="cb13-76"><a href="#cb13-76" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb13-77"><a href="#cb13-77" tabindex="-1"></a>    <span class="at">x =</span> <span class="st">&quot;Specification Number&quot;</span>,</span>
<span id="cb13-78"><a href="#cb13-78" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Variables&quot;</span></span>
<span id="cb13-79"><a href="#cb13-79" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb13-80"><a href="#cb13-80" tabindex="-1"></a>  <span class="fu">theme_minimal</span>() <span class="sc">+</span></span>
<span id="cb13-81"><a href="#cb13-81" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb13-82"><a href="#cb13-82" tabindex="-1"></a>    <span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">90</span>, <span class="at">vjust =</span> <span class="fl">0.5</span>, <span class="at">hjust =</span> <span class="dv">1</span>),</span>
<span id="cb13-83"><a href="#cb13-83" tabindex="-1"></a>    <span class="at">plot.margin =</span> <span class="fu">margin</span>(<span class="at">t =</span> <span class="dv">0</span>, <span class="at">r =</span> <span class="fl">5.5</span>, <span class="at">b =</span> <span class="fl">5.5</span>, <span class="at">l =</span> <span class="dv">30</span>)</span>
<span id="cb13-84"><a href="#cb13-84" tabindex="-1"></a>  )</span>
<span id="cb13-85"><a href="#cb13-85" tabindex="-1"></a></span>
<span id="cb13-86"><a href="#cb13-86" tabindex="-1"></a>g1 <span class="ot">&lt;-</span> <span class="fu">ggplotGrob</span>(upper_panel)</span>
<span id="cb13-87"><a href="#cb13-87" tabindex="-1"></a>g2 <span class="ot">&lt;-</span> <span class="fu">ggplotGrob</span>(lower_panel)</span>
<span id="cb13-88"><a href="#cb13-88" tabindex="-1"></a></span>
<span id="cb13-89"><a href="#cb13-89" tabindex="-1"></a>gw <span class="ot">&lt;-</span> grid<span class="sc">::</span><span class="fu">unit.pmax</span>(g1<span class="sc">$</span>widths, g2<span class="sc">$</span>widths)</span>
<span id="cb13-90"><a href="#cb13-90" tabindex="-1"></a>g1<span class="sc">$</span>widths <span class="ot">&lt;-</span> gw</span>
<span id="cb13-91"><a href="#cb13-91" tabindex="-1"></a>g2<span class="sc">$</span>widths <span class="ot">&lt;-</span> gw</span>
<span id="cb13-92"><a href="#cb13-92" tabindex="-1"></a></span>
<span id="cb13-93"><a href="#cb13-93" tabindex="-1"></a><span class="fu">grid.arrange</span>(g1, g2, <span class="at">ncol =</span> <span class="dv">1</span>, <span class="at">heights =</span> <span class="fu">c</span>(<span class="dv">2</span>, <span class="dv">1</span>))</span></code></pre></div>
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" width="672" /></p>
</div>
</div>
<div id="rolling-rdd" class="section level2 unnumbered">
<h2 class="unnumbered">Rolling RDD</h2>
<div id="table-4-rolling-rdd-estimates-around-the-first-gold-medal-shifting-the-cutoff-before-and-after-the-threshold-does-not-alter-the-null-results-for-cabinet-approval-and-feeling-thermometer" class="section level3 unnumbered">
<h3 class="unnumbered">Table 4: Rolling RDD estimates around the first
gold medal: Shifting the cutoff before and after the threshold does not
alter the null results for cabinet approval and feeling thermometer</h3>
</div>
<div id="table-5-rolling-rdd-estimates-around-the-second-gold-medal-shifting-the-cutoff-before-and-after-the-threshold-does-not-also-alter-the-null-results-for-cabinet-approval-and-feeling-thermometer" class="section level3 unnumbered">
<h3 class="unnumbered">Table 5: Rolling RDD estimates around the second
gold medal: Shifting the cutoff before and after the threshold does not
also alter the null results for cabinet approval and feeling
thermometer</h3>
<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1" tabindex="-1"></a>cutoff_diffs_1st <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">20</span>, <span class="sc">-</span><span class="dv">15</span>, <span class="sc">-</span><span class="dv">5</span>, <span class="sc">-</span><span class="dv">3</span>, <span class="sc">-</span><span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">15</span>, <span class="dv">20</span>)</span>
<span id="cb14-2"><a href="#cb14-2" tabindex="-1"></a>titles_1st <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;20 mins before&quot;</span>, <span class="st">&quot;15 mins before&quot;</span>, <span class="st">&quot;5 mins before&quot;</span>,</span>
<span id="cb14-3"><a href="#cb14-3" tabindex="-1"></a>                <span class="st">&quot;3 mins before&quot;</span>, <span class="st">&quot;1 min before&quot;</span>, <span class="st">&quot;At cutoff&quot;</span>,</span>
<span id="cb14-4"><a href="#cb14-4" tabindex="-1"></a>                <span class="st">&quot;1 min after&quot;</span>, <span class="st">&quot;3 mins after&quot;</span>, <span class="st">&quot;5 mins after&quot;</span>,</span>
<span id="cb14-5"><a href="#cb14-5" tabindex="-1"></a>                <span class="st">&quot;15 mins after&quot;</span>, <span class="st">&quot;20 mins after&quot;</span>)</span>
<span id="cb14-6"><a href="#cb14-6" tabindex="-1"></a></span>
<span id="cb14-7"><a href="#cb14-7" tabindex="-1"></a>cutoff_diffs_2nd <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">5</span>, <span class="sc">-</span><span class="dv">3</span>, <span class="sc">-</span><span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">15</span>, <span class="dv">20</span>)</span>
<span id="cb14-8"><a href="#cb14-8" tabindex="-1"></a>titles_2nd <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;5 mins before&quot;</span>, <span class="st">&quot;3 mins before&quot;</span>, <span class="st">&quot;1 min before&quot;</span>,</span>
<span id="cb14-9"><a href="#cb14-9" tabindex="-1"></a>                <span class="st">&quot;At cutoff&quot;</span>, <span class="st">&quot;1 min after&quot;</span>, <span class="st">&quot;3 mins after&quot;</span>,</span>
<span id="cb14-10"><a href="#cb14-10" tabindex="-1"></a>                <span class="st">&quot;5 mins after&quot;</span>, <span class="st">&quot;15 mins after&quot;</span>, <span class="st">&quot;20 mins after&quot;</span>)</span>
<span id="cb14-11"><a href="#cb14-11" tabindex="-1"></a></span>
<span id="cb14-12"><a href="#cb14-12" tabindex="-1"></a><span class="fu">stopifnot</span>(<span class="fu">length</span>(cutoff_diffs_1st) <span class="sc">==</span> <span class="fu">length</span>(titles_1st))</span>
<span id="cb14-13"><a href="#cb14-13" tabindex="-1"></a><span class="fu">stopifnot</span>(<span class="fu">length</span>(cutoff_diffs_2nd) <span class="sc">==</span> <span class="fu">length</span>(titles_2nd))</span>
<span id="cb14-14"><a href="#cb14-14" tabindex="-1"></a></span>
<span id="cb14-15"><a href="#cb14-15" tabindex="-1"></a></span>
<span id="cb14-16"><a href="#cb14-16" tabindex="-1"></a></span>
<span id="cb14-17"><a href="#cb14-17" tabindex="-1"></a>rdrobust_safe <span class="ot">&lt;-</span> <span class="cf">function</span>(y, x, covs, c,</span>
<span id="cb14-18"><a href="#cb14-18" tabindex="-1"></a>                          <span class="at">bwcheck_n =</span> <span class="dv">30</span>,</span>
<span id="cb14-19"><a href="#cb14-19" tabindex="-1"></a>                          <span class="at">h_fallback =</span> <span class="dv">40</span>) {</span>
<span id="cb14-20"><a href="#cb14-20" tabindex="-1"></a>  </span>
<span id="cb14-21"><a href="#cb14-21" tabindex="-1"></a>  fit <span class="ot">&lt;-</span> <span class="fu">tryCatch</span>(</span>
<span id="cb14-22"><a href="#cb14-22" tabindex="-1"></a>    <span class="fu">suppressWarnings</span>(</span>
<span id="cb14-23"><a href="#cb14-23" tabindex="-1"></a>      <span class="fu">rdrobust</span>(</span>
<span id="cb14-24"><a href="#cb14-24" tabindex="-1"></a>        <span class="at">y =</span> y, <span class="at">x =</span> x, <span class="at">c =</span> c,</span>
<span id="cb14-25"><a href="#cb14-25" tabindex="-1"></a>        <span class="at">covs =</span> covs,</span>
<span id="cb14-26"><a href="#cb14-26" tabindex="-1"></a>        <span class="at">masspoints =</span> <span class="st">&quot;adjust&quot;</span>,</span>
<span id="cb14-27"><a href="#cb14-27" tabindex="-1"></a>        <span class="at">bwcheck =</span> bwcheck_n</span>
<span id="cb14-28"><a href="#cb14-28" tabindex="-1"></a>      )</span>
<span id="cb14-29"><a href="#cb14-29" tabindex="-1"></a>    ),</span>
<span id="cb14-30"><a href="#cb14-30" tabindex="-1"></a>    <span class="at">error =</span> <span class="cf">function</span>(e) <span class="cn">NULL</span></span>
<span id="cb14-31"><a href="#cb14-31" tabindex="-1"></a>  )</span>
<span id="cb14-32"><a href="#cb14-32" tabindex="-1"></a>  </span>
<span id="cb14-33"><a href="#cb14-33" tabindex="-1"></a>  used_fallback <span class="ot">&lt;-</span> <span class="cn">FALSE</span></span>
<span id="cb14-34"><a href="#cb14-34" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">is.null</span>(fit)) {</span>
<span id="cb14-35"><a href="#cb14-35" tabindex="-1"></a>    used_fallback <span class="ot">&lt;-</span> <span class="cn">TRUE</span></span>
<span id="cb14-36"><a href="#cb14-36" tabindex="-1"></a>    fit <span class="ot">&lt;-</span> <span class="fu">tryCatch</span>(</span>
<span id="cb14-37"><a href="#cb14-37" tabindex="-1"></a>      <span class="fu">suppressWarnings</span>(</span>
<span id="cb14-38"><a href="#cb14-38" tabindex="-1"></a>        <span class="fu">rdrobust</span>(</span>
<span id="cb14-39"><a href="#cb14-39" tabindex="-1"></a>          <span class="at">y =</span> y, <span class="at">x =</span> x, <span class="at">c =</span> c,</span>
<span id="cb14-40"><a href="#cb14-40" tabindex="-1"></a>          <span class="at">covs =</span> covs,</span>
<span id="cb14-41"><a href="#cb14-41" tabindex="-1"></a>          <span class="at">masspoints =</span> <span class="st">&quot;adjust&quot;</span>,</span>
<span id="cb14-42"><a href="#cb14-42" tabindex="-1"></a>          <span class="at">h =</span> h_fallback</span>
<span id="cb14-43"><a href="#cb14-43" tabindex="-1"></a>        )</span>
<span id="cb14-44"><a href="#cb14-44" tabindex="-1"></a>      ),</span>
<span id="cb14-45"><a href="#cb14-45" tabindex="-1"></a>      <span class="at">error =</span> <span class="cf">function</span>(e) <span class="cn">NULL</span></span>
<span id="cb14-46"><a href="#cb14-46" tabindex="-1"></a>    )</span>
<span id="cb14-47"><a href="#cb14-47" tabindex="-1"></a>  }</span>
<span id="cb14-48"><a href="#cb14-48" tabindex="-1"></a>  </span>
<span id="cb14-49"><a href="#cb14-49" tabindex="-1"></a>  <span class="fu">list</span>(<span class="at">fit =</span> fit, <span class="at">used_fallback =</span> used_fallback)</span>
<span id="cb14-50"><a href="#cb14-50" tabindex="-1"></a>}</span>
<span id="cb14-51"><a href="#cb14-51" tabindex="-1"></a></span>
<span id="cb14-52"><a href="#cb14-52" tabindex="-1"></a></span>
<span id="cb14-53"><a href="#cb14-53" tabindex="-1"></a>extract_rdrobust_for_table <span class="ot">&lt;-</span> <span class="cf">function</span>(fit_obj) {</span>
<span id="cb14-54"><a href="#cb14-54" tabindex="-1"></a>  </span>
<span id="cb14-55"><a href="#cb14-55" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">is.null</span>(fit_obj)) {</span>
<span id="cb14-56"><a href="#cb14-56" tabindex="-1"></a>    <span class="fu">return</span>(<span class="fu">tibble</span>(</span>
<span id="cb14-57"><a href="#cb14-57" tabindex="-1"></a>      <span class="at">tau =</span> <span class="cn">NA_real_</span>, <span class="at">se =</span> <span class="cn">NA_real_</span>, <span class="at">p =</span> <span class="cn">NA_real_</span>,</span>
<span id="cb14-58"><a href="#cb14-58" tabindex="-1"></a>      <span class="at">ci_low =</span> <span class="cn">NA_real_</span>, <span class="at">ci_high =</span> <span class="cn">NA_real_</span>,</span>
<span id="cb14-59"><a href="#cb14-59" tabindex="-1"></a>      <span class="at">h_left =</span> <span class="cn">NA_real_</span>, <span class="at">h_right =</span> <span class="cn">NA_real_</span>,</span>
<span id="cb14-60"><a href="#cb14-60" tabindex="-1"></a>      <span class="at">N_left =</span> <span class="cn">NA_real_</span>, <span class="at">N_right =</span> <span class="cn">NA_real_</span></span>
<span id="cb14-61"><a href="#cb14-61" tabindex="-1"></a>    ))</span>
<span id="cb14-62"><a href="#cb14-62" tabindex="-1"></a>  }</span>
<span id="cb14-63"><a href="#cb14-63" tabindex="-1"></a>  </span>
<span id="cb14-64"><a href="#cb14-64" tabindex="-1"></a>  tau <span class="ot">&lt;-</span> <span class="cn">NA_real_</span></span>
<span id="cb14-65"><a href="#cb14-65" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(fit_obj<span class="sc">$</span>Estimate) <span class="sc">&amp;&amp;</span> <span class="fu">is.matrix</span>(fit_obj<span class="sc">$</span>Estimate)) {</span>
<span id="cb14-66"><a href="#cb14-66" tabindex="-1"></a>    cn <span class="ot">&lt;-</span> <span class="fu">colnames</span>(fit_obj<span class="sc">$</span>Estimate)</span>
<span id="cb14-67"><a href="#cb14-67" tabindex="-1"></a>    <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(cn) <span class="sc">&amp;&amp;</span> <span class="st">&quot;tau.bc&quot;</span> <span class="sc">%in%</span> cn) {</span>
<span id="cb14-68"><a href="#cb14-68" tabindex="-1"></a>      tau <span class="ot">&lt;-</span> fit_obj<span class="sc">$</span>Estimate[<span class="dv">1</span>, <span class="st">&quot;tau.bc&quot;</span>]</span>
<span id="cb14-69"><a href="#cb14-69" tabindex="-1"></a>    } <span class="cf">else</span> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(cn) <span class="sc">&amp;&amp;</span> <span class="st">&quot;tau.us&quot;</span> <span class="sc">%in%</span> cn) {</span>
<span id="cb14-70"><a href="#cb14-70" tabindex="-1"></a>      tau <span class="ot">&lt;-</span> fit_obj<span class="sc">$</span>Estimate[<span class="dv">1</span>, <span class="st">&quot;tau.us&quot;</span>]</span>
<span id="cb14-71"><a href="#cb14-71" tabindex="-1"></a>    }</span>
<span id="cb14-72"><a href="#cb14-72" tabindex="-1"></a>  }</span>
<span id="cb14-73"><a href="#cb14-73" tabindex="-1"></a>  </span>
<span id="cb14-74"><a href="#cb14-74" tabindex="-1"></a>  se <span class="ot">&lt;-</span> <span class="cn">NA_real_</span></span>
<span id="cb14-75"><a href="#cb14-75" tabindex="-1"></a>  p  <span class="ot">&lt;-</span> <span class="cn">NA_real_</span></span>
<span id="cb14-76"><a href="#cb14-76" tabindex="-1"></a>  ciL <span class="ot">&lt;-</span> <span class="cn">NA_real_</span></span>
<span id="cb14-77"><a href="#cb14-77" tabindex="-1"></a>  ciU <span class="ot">&lt;-</span> <span class="cn">NA_real_</span></span>
<span id="cb14-78"><a href="#cb14-78" tabindex="-1"></a>  </span>
<span id="cb14-79"><a href="#cb14-79" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(fit_obj<span class="sc">$</span>se) <span class="sc">&amp;&amp;</span> <span class="sc">!</span><span class="fu">is.null</span>(<span class="fu">rownames</span>(fit_obj<span class="sc">$</span>se)) <span class="sc">&amp;&amp;</span> <span class="st">&quot;Robust&quot;</span> <span class="sc">%in%</span> <span class="fu">rownames</span>(fit_obj<span class="sc">$</span>se)) {</span>
<span id="cb14-80"><a href="#cb14-80" tabindex="-1"></a>    se <span class="ot">&lt;-</span> fit_obj<span class="sc">$</span>se[<span class="st">&quot;Robust&quot;</span>, <span class="dv">1</span>]</span>
<span id="cb14-81"><a href="#cb14-81" tabindex="-1"></a>  }</span>
<span id="cb14-82"><a href="#cb14-82" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(fit_obj<span class="sc">$</span>pv) <span class="sc">&amp;&amp;</span> <span class="sc">!</span><span class="fu">is.null</span>(<span class="fu">rownames</span>(fit_obj<span class="sc">$</span>pv)) <span class="sc">&amp;&amp;</span> <span class="st">&quot;Robust&quot;</span> <span class="sc">%in%</span> <span class="fu">rownames</span>(fit_obj<span class="sc">$</span>pv)) {</span>
<span id="cb14-83"><a href="#cb14-83" tabindex="-1"></a>    p <span class="ot">&lt;-</span> fit_obj<span class="sc">$</span>pv[<span class="st">&quot;Robust&quot;</span>, <span class="dv">1</span>]</span>
<span id="cb14-84"><a href="#cb14-84" tabindex="-1"></a>  }</span>
<span id="cb14-85"><a href="#cb14-85" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(fit_obj<span class="sc">$</span>ci) <span class="sc">&amp;&amp;</span> <span class="sc">!</span><span class="fu">is.null</span>(<span class="fu">rownames</span>(fit_obj<span class="sc">$</span>ci)) <span class="sc">&amp;&amp;</span> <span class="st">&quot;Robust&quot;</span> <span class="sc">%in%</span> <span class="fu">rownames</span>(fit_obj<span class="sc">$</span>ci)) {</span>
<span id="cb14-86"><a href="#cb14-86" tabindex="-1"></a>    ciL <span class="ot">&lt;-</span> fit_obj<span class="sc">$</span>ci[<span class="st">&quot;Robust&quot;</span>, <span class="dv">1</span>]</span>
<span id="cb14-87"><a href="#cb14-87" tabindex="-1"></a>    ciU <span class="ot">&lt;-</span> fit_obj<span class="sc">$</span>ci[<span class="st">&quot;Robust&quot;</span>, <span class="dv">2</span>]</span>
<span id="cb14-88"><a href="#cb14-88" tabindex="-1"></a>  }</span>
<span id="cb14-89"><a href="#cb14-89" tabindex="-1"></a>  </span>
<span id="cb14-90"><a href="#cb14-90" tabindex="-1"></a>  hL <span class="ot">&lt;-</span> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(fit_obj<span class="sc">$</span>bws)) fit_obj<span class="sc">$</span>bws[<span class="dv">1</span>, <span class="dv">1</span>] <span class="cf">else</span> <span class="cn">NA_real_</span></span>
<span id="cb14-91"><a href="#cb14-91" tabindex="-1"></a>  hR <span class="ot">&lt;-</span> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(fit_obj<span class="sc">$</span>bws)) fit_obj<span class="sc">$</span>bws[<span class="dv">1</span>, <span class="dv">2</span>] <span class="cf">else</span> <span class="cn">NA_real_</span></span>
<span id="cb14-92"><a href="#cb14-92" tabindex="-1"></a>  </span>
<span id="cb14-93"><a href="#cb14-93" tabindex="-1"></a>  NL <span class="ot">&lt;-</span> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(fit_obj<span class="sc">$</span>N_h)) fit_obj<span class="sc">$</span>N_h[<span class="dv">1</span>] <span class="cf">else</span> <span class="cn">NA_real_</span></span>
<span id="cb14-94"><a href="#cb14-94" tabindex="-1"></a>  NR <span class="ot">&lt;-</span> <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.null</span>(fit_obj<span class="sc">$</span>N_h)) fit_obj<span class="sc">$</span>N_h[<span class="dv">2</span>] <span class="cf">else</span> <span class="cn">NA_real_</span></span>
<span id="cb14-95"><a href="#cb14-95" tabindex="-1"></a>  </span>
<span id="cb14-96"><a href="#cb14-96" tabindex="-1"></a>  <span class="fu">tibble</span>(</span>
<span id="cb14-97"><a href="#cb14-97" tabindex="-1"></a>    <span class="at">tau =</span> tau, <span class="at">se =</span> se, <span class="at">p =</span> p,</span>
<span id="cb14-98"><a href="#cb14-98" tabindex="-1"></a>    <span class="at">ci_low =</span> ciL, <span class="at">ci_high =</span> ciU,</span>
<span id="cb14-99"><a href="#cb14-99" tabindex="-1"></a>    <span class="at">h_left =</span> hL, <span class="at">h_right =</span> hR,</span>
<span id="cb14-100"><a href="#cb14-100" tabindex="-1"></a>    <span class="at">N_left =</span> NL, <span class="at">N_right =</span> NR</span>
<span id="cb14-101"><a href="#cb14-101" tabindex="-1"></a>  )</span>
<span id="cb14-102"><a href="#cb14-102" tabindex="-1"></a>}</span>
<span id="cb14-103"><a href="#cb14-103" tabindex="-1"></a></span>
<span id="cb14-104"><a href="#cb14-104" tabindex="-1"></a>fmt_p <span class="ot">&lt;-</span> <span class="cf">function</span>(p) {</span>
<span id="cb14-105"><a href="#cb14-105" tabindex="-1"></a>  out <span class="ot">&lt;-</span> <span class="fu">rep</span>(<span class="st">&quot;NA&quot;</span>, <span class="fu">length</span>(p))</span>
<span id="cb14-106"><a href="#cb14-106" tabindex="-1"></a>  ok <span class="ot">&lt;-</span> <span class="sc">!</span><span class="fu">is.na</span>(p)</span>
<span id="cb14-107"><a href="#cb14-107" tabindex="-1"></a>  out[ok <span class="sc">&amp;</span> p <span class="sc">&lt;</span> <span class="fl">0.001</span>] <span class="ot">&lt;-</span> <span class="st">&quot;$&lt;0.001$&quot;</span></span>
<span id="cb14-108"><a href="#cb14-108" tabindex="-1"></a>  out[ok <span class="sc">&amp;</span> p <span class="sc">&gt;=</span> <span class="fl">0.001</span>] <span class="ot">&lt;-</span> <span class="fu">sprintf</span>(<span class="st">&quot;%.3f&quot;</span>, p[ok <span class="sc">&amp;</span> p <span class="sc">&gt;=</span> <span class="fl">0.001</span>])</span>
<span id="cb14-109"><a href="#cb14-109" tabindex="-1"></a>  out</span>
<span id="cb14-110"><a href="#cb14-110" tabindex="-1"></a>}</span>
<span id="cb14-111"><a href="#cb14-111" tabindex="-1"></a></span>
<span id="cb14-112"><a href="#cb14-112" tabindex="-1"></a>fmt_est <span class="ot">&lt;-</span> <span class="cf">function</span>(tau, p) {</span>
<span id="cb14-113"><a href="#cb14-113" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">is.na</span>(tau)) <span class="fu">return</span>(<span class="st">&quot;NA&quot;</span>)</span>
<span id="cb14-114"><a href="#cb14-114" tabindex="-1"></a>  stars <span class="ot">&lt;-</span> <span class="st">&quot;&quot;</span></span>
<span id="cb14-115"><a href="#cb14-115" tabindex="-1"></a>  <span class="cf">if</span> (<span class="sc">!</span><span class="fu">is.na</span>(p)) {</span>
<span id="cb14-116"><a href="#cb14-116" tabindex="-1"></a>    <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.01</span>) stars <span class="ot">&lt;-</span> <span class="st">&quot;***&quot;</span></span>
<span id="cb14-117"><a href="#cb14-117" tabindex="-1"></a>    <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.05</span>) stars <span class="ot">&lt;-</span> <span class="st">&quot;**&quot;</span></span>
<span id="cb14-118"><a href="#cb14-118" tabindex="-1"></a>    <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.10</span>) stars <span class="ot">&lt;-</span> <span class="st">&quot;*&quot;</span></span>
<span id="cb14-119"><a href="#cb14-119" tabindex="-1"></a>  }</span>
<span id="cb14-120"><a href="#cb14-120" tabindex="-1"></a>  <span class="fu">sprintf</span>(<span class="st">&quot;%.3f%s&quot;</span>, tau, stars)</span>
<span id="cb14-121"><a href="#cb14-121" tabindex="-1"></a>}</span>
<span id="cb14-122"><a href="#cb14-122" tabindex="-1"></a></span>
<span id="cb14-123"><a href="#cb14-123" tabindex="-1"></a></span>
<span id="cb14-124"><a href="#cb14-124" tabindex="-1"></a></span>
<span id="cb14-125"><a href="#cb14-125" tabindex="-1"></a>build_rolling_table_for_render <span class="ot">&lt;-</span> <span class="cf">function</span>(df, outcome_var, running_var,</span>
<span id="cb14-126"><a href="#cb14-126" tabindex="-1"></a>                                           cutoff_diffs, titles,</span>
<span id="cb14-127"><a href="#cb14-127" tabindex="-1"></a>                                           <span class="at">bwcheck_n =</span> <span class="dv">30</span>, <span class="at">h_fallback =</span> <span class="dv">40</span>) {</span>
<span id="cb14-128"><a href="#cb14-128" tabindex="-1"></a>  </span>
<span id="cb14-129"><a href="#cb14-129" tabindex="-1"></a>  df_cc <span class="ot">&lt;-</span> df <span class="sc">%&gt;%</span></span>
<span id="cb14-130"><a href="#cb14-130" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">select</span>(dplyr<span class="sc">::</span><span class="fu">all_of</span>(outcome_var),</span>
<span id="cb14-131"><a href="#cb14-131" tabindex="-1"></a>                  dplyr<span class="sc">::</span><span class="fu">all_of</span>(running_var),</span>
<span id="cb14-132"><a href="#cb14-132" tabindex="-1"></a>                  age, female, income, education, psu_rul) <span class="sc">%&gt;%</span></span>
<span id="cb14-133"><a href="#cb14-133" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">filter</span>(dplyr<span class="sc">::</span><span class="fu">if_all</span>(dplyr<span class="sc">::</span><span class="fu">everything</span>(), <span class="sc">~</span> <span class="fu">is.finite</span>(.)))</span>
<span id="cb14-134"><a href="#cb14-134" tabindex="-1"></a>  </span>
<span id="cb14-135"><a href="#cb14-135" tabindex="-1"></a>  y <span class="ot">&lt;-</span> df_cc[[outcome_var]]</span>
<span id="cb14-136"><a href="#cb14-136" tabindex="-1"></a>  x <span class="ot">&lt;-</span> df_cc[[running_var]]</span>
<span id="cb14-137"><a href="#cb14-137" tabindex="-1"></a>  covs <span class="ot">&lt;-</span> df_cc <span class="sc">%&gt;%</span></span>
<span id="cb14-138"><a href="#cb14-138" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">select</span>(age, female, income, education, psu_rul) <span class="sc">%&gt;%</span></span>
<span id="cb14-139"><a href="#cb14-139" tabindex="-1"></a>    <span class="fu">as.matrix</span>()</span>
<span id="cb14-140"><a href="#cb14-140" tabindex="-1"></a>  </span>
<span id="cb14-141"><a href="#cb14-141" tabindex="-1"></a>  out_list <span class="ot">&lt;-</span> <span class="fu">vector</span>(<span class="st">&quot;list&quot;</span>, <span class="fu">length</span>(cutoff_diffs))</span>
<span id="cb14-142"><a href="#cb14-142" tabindex="-1"></a>  </span>
<span id="cb14-143"><a href="#cb14-143" tabindex="-1"></a>  <span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">seq_along</span>(cutoff_diffs)) {</span>
<span id="cb14-144"><a href="#cb14-144" tabindex="-1"></a>    tmp <span class="ot">&lt;-</span> <span class="fu">rdrobust_safe</span>(</span>
<span id="cb14-145"><a href="#cb14-145" tabindex="-1"></a>      <span class="at">y =</span> y, <span class="at">x =</span> x, <span class="at">covs =</span> covs, <span class="at">c =</span> cutoff_diffs[i],</span>
<span id="cb14-146"><a href="#cb14-146" tabindex="-1"></a>      <span class="at">bwcheck_n =</span> bwcheck_n, <span class="at">h_fallback =</span> h_fallback</span>
<span id="cb14-147"><a href="#cb14-147" tabindex="-1"></a>    )</span>
<span id="cb14-148"><a href="#cb14-148" tabindex="-1"></a>    </span>
<span id="cb14-149"><a href="#cb14-149" tabindex="-1"></a>    est <span class="ot">&lt;-</span> <span class="fu">extract_rdrobust_for_table</span>(tmp<span class="sc">$</span>fit) <span class="sc">%&gt;%</span></span>
<span id="cb14-150"><a href="#cb14-150" tabindex="-1"></a>      dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb14-151"><a href="#cb14-151" tabindex="-1"></a>        <span class="at">cutoff_label =</span> titles[i],</span>
<span id="cb14-152"><a href="#cb14-152" tabindex="-1"></a>        <span class="at">c_mins =</span> cutoff_diffs[i]</span>
<span id="cb14-153"><a href="#cb14-153" tabindex="-1"></a>      )</span>
<span id="cb14-154"><a href="#cb14-154" tabindex="-1"></a>    </span>
<span id="cb14-155"><a href="#cb14-155" tabindex="-1"></a>    out_list[[i]] <span class="ot">&lt;-</span> est</span>
<span id="cb14-156"><a href="#cb14-156" tabindex="-1"></a>  }</span>
<span id="cb14-157"><a href="#cb14-157" tabindex="-1"></a>  </span>
<span id="cb14-158"><a href="#cb14-158" tabindex="-1"></a>  tab_raw <span class="ot">&lt;-</span> dplyr<span class="sc">::</span><span class="fu">bind_rows</span>(out_list)</span>
<span id="cb14-159"><a href="#cb14-159" tabindex="-1"></a>  </span>
<span id="cb14-160"><a href="#cb14-160" tabindex="-1"></a>  tab_fmt <span class="ot">&lt;-</span> tab_raw <span class="sc">%&gt;%</span></span>
<span id="cb14-161"><a href="#cb14-161" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb14-162"><a href="#cb14-162" tabindex="-1"></a>      <span class="at">RD_estimate =</span> <span class="fu">mapply</span>(fmt_est, tau, p, <span class="at">SIMPLIFY =</span> <span class="cn">TRUE</span>),</span>
<span id="cb14-163"><a href="#cb14-163" tabindex="-1"></a>      <span class="at">SE =</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(se), <span class="st">&quot;NA&quot;</span>, <span class="fu">sprintf</span>(<span class="st">&quot;(%.3f)&quot;</span>, se)),</span>
<span id="cb14-164"><a href="#cb14-164" tabindex="-1"></a>      <span class="at">CI =</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(ci_low), <span class="st">&quot;NA&quot;</span>, <span class="fu">sprintf</span>(<span class="st">&quot;[%.3f, %.3f]&quot;</span>, ci_low, ci_high)),</span>
<span id="cb14-165"><a href="#cb14-165" tabindex="-1"></a>      <span class="at">p_value =</span> <span class="fu">fmt_p</span>(p),</span>
<span id="cb14-166"><a href="#cb14-166" tabindex="-1"></a>      <span class="at">h =</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(h_left), <span class="st">&quot;NA&quot;</span>, <span class="fu">sprintf</span>(<span class="st">&quot;%.1f / %.1f&quot;</span>, h_left, h_right)),</span>
<span id="cb14-167"><a href="#cb14-167" tabindex="-1"></a>      <span class="at">N_L =</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(N_left), <span class="st">&quot;NA&quot;</span>, <span class="fu">as.character</span>(N_left)),</span>
<span id="cb14-168"><a href="#cb14-168" tabindex="-1"></a>      <span class="at">N_R =</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(N_right), <span class="st">&quot;NA&quot;</span>, <span class="fu">as.character</span>(N_right))</span>
<span id="cb14-169"><a href="#cb14-169" tabindex="-1"></a>    ) <span class="sc">%&gt;%</span></span>
<span id="cb14-170"><a href="#cb14-170" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">transmute</span>(</span>
<span id="cb14-171"><a href="#cb14-171" tabindex="-1"></a>      <span class="st">`</span><span class="at">Pseudo cutoff</span><span class="st">`</span> <span class="ot">=</span> cutoff_label,</span>
<span id="cb14-172"><a href="#cb14-172" tabindex="-1"></a>      <span class="st">`</span><span class="at">$c$ (mins)</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">as.character</span>(c_mins),</span>
<span id="cb14-173"><a href="#cb14-173" tabindex="-1"></a>      <span class="st">`</span><span class="at">RD estimate</span><span class="st">`</span> <span class="ot">=</span> RD_estimate,</span>
<span id="cb14-174"><a href="#cb14-174" tabindex="-1"></a>      <span class="st">`</span><span class="at">SE</span><span class="st">`</span> <span class="ot">=</span> SE,</span>
<span id="cb14-175"><a href="#cb14-175" tabindex="-1"></a>      <span class="st">`</span><span class="at">95</span><span class="sc">\\</span><span class="at">% CI</span><span class="st">`</span> <span class="ot">=</span> CI,</span>
<span id="cb14-176"><a href="#cb14-176" tabindex="-1"></a>      <span class="st">`</span><span class="at">$p$</span><span class="st">`</span> <span class="ot">=</span> p_value,</span>
<span id="cb14-177"><a href="#cb14-177" tabindex="-1"></a>      <span class="st">`</span><span class="at">$h_L/h_R$</span><span class="st">`</span> <span class="ot">=</span> h,</span>
<span id="cb14-178"><a href="#cb14-178" tabindex="-1"></a>      <span class="st">`</span><span class="at">$N_L$</span><span class="st">`</span> <span class="ot">=</span> N_L,</span>
<span id="cb14-179"><a href="#cb14-179" tabindex="-1"></a>      <span class="st">`</span><span class="at">$N_R$</span><span class="st">`</span> <span class="ot">=</span> N_R</span>
<span id="cb14-180"><a href="#cb14-180" tabindex="-1"></a>    )</span>
<span id="cb14-181"><a href="#cb14-181" tabindex="-1"></a>  </span>
<span id="cb14-182"><a href="#cb14-182" tabindex="-1"></a>  tab_fmt</span>
<span id="cb14-183"><a href="#cb14-183" tabindex="-1"></a>}</span>
<span id="cb14-184"><a href="#cb14-184" tabindex="-1"></a></span>
<span id="cb14-185"><a href="#cb14-185" tabindex="-1"></a></span>
<span id="cb14-186"><a href="#cb14-186" tabindex="-1"></a>tab48_cabap <span class="ot">&lt;-</span> <span class="fu">build_rolling_table_for_render</span>(</span>
<span id="cb14-187"><a href="#cb14-187" tabindex="-1"></a>  <span class="at">df =</span> df_rdd48,</span>
<span id="cb14-188"><a href="#cb14-188" tabindex="-1"></a>  <span class="at">outcome_var =</span> <span class="st">&quot;cabap&quot;</span>,</span>
<span id="cb14-189"><a href="#cb14-189" tabindex="-1"></a>  <span class="at">running_var =</span> <span class="st">&quot;running_var&quot;</span>,</span>
<span id="cb14-190"><a href="#cb14-190" tabindex="-1"></a>  <span class="at">cutoff_diffs =</span> cutoff_diffs_1st,</span>
<span id="cb14-191"><a href="#cb14-191" tabindex="-1"></a>  <span class="at">titles =</span> titles_1st,</span>
<span id="cb14-192"><a href="#cb14-192" tabindex="-1"></a>  <span class="at">bwcheck_n =</span> <span class="dv">30</span>,</span>
<span id="cb14-193"><a href="#cb14-193" tabindex="-1"></a>  <span class="at">h_fallback =</span> <span class="dv">40</span></span>
<span id="cb14-194"><a href="#cb14-194" tabindex="-1"></a>)</span>
<span id="cb14-195"><a href="#cb14-195" tabindex="-1"></a></span>
<span id="cb14-196"><a href="#cb14-196" tabindex="-1"></a>tab48_ftj <span class="ot">&lt;-</span> <span class="fu">build_rolling_table_for_render</span>(</span>
<span id="cb14-197"><a href="#cb14-197" tabindex="-1"></a>  <span class="at">df =</span> df_rdd48,</span>
<span id="cb14-198"><a href="#cb14-198" tabindex="-1"></a>  <span class="at">outcome_var =</span> <span class="st">&quot;ftj&quot;</span>,</span>
<span id="cb14-199"><a href="#cb14-199" tabindex="-1"></a>  <span class="at">running_var =</span> <span class="st">&quot;running_var&quot;</span>,</span>
<span id="cb14-200"><a href="#cb14-200" tabindex="-1"></a>  <span class="at">cutoff_diffs =</span> cutoff_diffs_1st,</span>
<span id="cb14-201"><a href="#cb14-201" tabindex="-1"></a>  <span class="at">titles =</span> titles_1st,</span>
<span id="cb14-202"><a href="#cb14-202" tabindex="-1"></a>  <span class="at">bwcheck_n =</span> <span class="dv">30</span>,</span>
<span id="cb14-203"><a href="#cb14-203" tabindex="-1"></a>  <span class="at">h_fallback =</span> <span class="dv">40</span></span>
<span id="cb14-204"><a href="#cb14-204" tabindex="-1"></a>)</span>
<span id="cb14-205"><a href="#cb14-205" tabindex="-1"></a></span>
<span id="cb14-206"><a href="#cb14-206" tabindex="-1"></a>tab66_cabap <span class="ot">&lt;-</span> <span class="fu">build_rolling_table_for_render</span>(</span>
<span id="cb14-207"><a href="#cb14-207" tabindex="-1"></a>  <span class="at">df =</span> df_rdd66,</span>
<span id="cb14-208"><a href="#cb14-208" tabindex="-1"></a>  <span class="at">outcome_var =</span> <span class="st">&quot;cabap&quot;</span>,</span>
<span id="cb14-209"><a href="#cb14-209" tabindex="-1"></a>  <span class="at">running_var =</span> <span class="st">&quot;running_var2&quot;</span>,</span>
<span id="cb14-210"><a href="#cb14-210" tabindex="-1"></a>  <span class="at">cutoff_diffs =</span> cutoff_diffs_2nd,</span>
<span id="cb14-211"><a href="#cb14-211" tabindex="-1"></a>  <span class="at">titles =</span> titles_2nd,</span>
<span id="cb14-212"><a href="#cb14-212" tabindex="-1"></a>  <span class="at">bwcheck_n =</span> <span class="dv">30</span>,</span>
<span id="cb14-213"><a href="#cb14-213" tabindex="-1"></a>  <span class="at">h_fallback =</span> <span class="dv">40</span></span>
<span id="cb14-214"><a href="#cb14-214" tabindex="-1"></a>)</span>
<span id="cb14-215"><a href="#cb14-215" tabindex="-1"></a></span>
<span id="cb14-216"><a href="#cb14-216" tabindex="-1"></a>tab66_ftj <span class="ot">&lt;-</span> <span class="fu">build_rolling_table_for_render</span>(</span>
<span id="cb14-217"><a href="#cb14-217" tabindex="-1"></a>  <span class="at">df =</span> df_rdd66,</span>
<span id="cb14-218"><a href="#cb14-218" tabindex="-1"></a>  <span class="at">outcome_var =</span> <span class="st">&quot;ftj&quot;</span>,</span>
<span id="cb14-219"><a href="#cb14-219" tabindex="-1"></a>  <span class="at">running_var =</span> <span class="st">&quot;running_var2&quot;</span>,</span>
<span id="cb14-220"><a href="#cb14-220" tabindex="-1"></a>  <span class="at">cutoff_diffs =</span> cutoff_diffs_2nd,</span>
<span id="cb14-221"><a href="#cb14-221" tabindex="-1"></a>  <span class="at">titles =</span> titles_2nd,</span>
<span id="cb14-222"><a href="#cb14-222" tabindex="-1"></a>  <span class="at">bwcheck_n =</span> <span class="dv">30</span>,</span>
<span id="cb14-223"><a href="#cb14-223" tabindex="-1"></a>  <span class="at">h_fallback =</span> <span class="dv">40</span></span>
<span id="cb14-224"><a href="#cb14-224" tabindex="-1"></a>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
Pseudo cutoff
</th>
<th style="text-align:center;font-weight: bold;">
<span class="math inline">\(c\)</span> (mins)
</th>
<th style="text-align:center;font-weight: bold;">
RD estimate
</th>
<th style="text-align:center;font-weight: bold;">
SE
</th>
<th style="text-align:center;font-weight: bold;">
95% CI
</th>
<th style="text-align:center;font-weight: bold;">
<span class="math inline">\(p\)</span>
</th>
<th style="text-align:center;font-weight: bold;">
<span class="math inline">\(h_L/h_R\)</span>
</th>
<th style="text-align:center;font-weight: bold;">
<span class="math inline">\(N_L\)</span>
</th>
<th style="text-align:center;font-weight: bold;">
<span class="math inline">\(N_R\)</span>
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">

</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
</tr>
<tr>
<td style="text-align:left;">
20 mins before
</td>
<td style="text-align:center;">
-20
</td>
<td style="text-align:center;">
-0.167*
</td>
<td style="text-align:center;">
(0.092)
</td>
<td style="text-align:center;">
[-0.349, 0.014]
</td>
<td style="text-align:center;">
0.070
</td>
<td style="text-align:center;">
27.0 / 27.0
</td>
<td style="text-align:center;">
373
</td>
<td style="text-align:center;">
223
</td>
</tr>
<tr>
<td style="text-align:left;">
15 mins before
</td>
<td style="text-align:center;">
-15
</td>
<td style="text-align:center;">
0.042
</td>
<td style="text-align:center;">
(0.113)
</td>
<td style="text-align:center;">
[-0.178, 0.263]
</td>
<td style="text-align:center;">
0.708
</td>
<td style="text-align:center;">
30.2 / 30.2
</td>
<td style="text-align:center;">
535
</td>
<td style="text-align:center;">
99
</td>
</tr>
<tr>
<td style="text-align:left;">
5 mins before
</td>
<td style="text-align:center;">
-5
</td>
<td style="text-align:center;">
-0.465
</td>
<td style="text-align:center;">
(0.600)
</td>
<td style="text-align:center;">
[-1.640, 0.710]
</td>
<td style="text-align:center;">
0.438
</td>
<td style="text-align:center;">
14.8 / 14.8
</td>
<td style="text-align:center;">
126
</td>
<td style="text-align:center;">
54
</td>
</tr>
<tr>
<td style="text-align:left;">
3 mins before
</td>
<td style="text-align:center;">
-3
</td>
<td style="text-align:center;">
-2.534
</td>
<td style="text-align:center;">
(1.647)
</td>
<td style="text-align:center;">
[-5.763, 0.694]
</td>
<td style="text-align:center;">
0.124
</td>
<td style="text-align:center;">
13.7 / 13.7
</td>
<td style="text-align:center;">
36
</td>
<td style="text-align:center;">
57
</td>
</tr>
<tr>
<td style="text-align:left;">
1 min before
</td>
<td style="text-align:center;">
-1
</td>
<td style="text-align:center;">
0.010
</td>
<td style="text-align:center;">
(0.496)
</td>
<td style="text-align:center;">
[-0.962, 0.982]
</td>
<td style="text-align:center;">
0.984
</td>
<td style="text-align:center;">
40.0 / 40.0
</td>
<td style="text-align:center;">
557
</td>
<td style="text-align:center;">
94
</td>
</tr>
<tr>
<td style="text-align:left;">
At cutoff
</td>
<td style="text-align:center;">
0
</td>
<td style="text-align:center;">
0.270
</td>
<td style="text-align:center;">
(0.504)
</td>
<td style="text-align:center;">
[-0.719, 1.259]
</td>
<td style="text-align:center;">
0.592
</td>
<td style="text-align:center;">
21.1 / 21.1
</td>
<td style="text-align:center;">
225
</td>
<td style="text-align:center;">
79
</td>
</tr>
<tr>
<td style="text-align:left;">
1 min after
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
0.026
</td>
<td style="text-align:center;">
(0.253)
</td>
<td style="text-align:center;">
[-0.469, 0.521]
</td>
<td style="text-align:center;">
0.919
</td>
<td style="text-align:center;">
32.7 / 32.7
</td>
<td style="text-align:center;">
563
</td>
<td style="text-align:center;">
84
</td>
</tr>
<tr>
<td style="text-align:left;">
3 mins after
</td>
<td style="text-align:center;">
3
</td>
<td style="text-align:center;">
-0.100
</td>
<td style="text-align:center;">
(0.170)
</td>
<td style="text-align:center;">
[-0.433, 0.233]
</td>
<td style="text-align:center;">
0.557
</td>
<td style="text-align:center;">
41.5 / 41.5
</td>
<td style="text-align:center;">
571
</td>
<td style="text-align:center;">
81
</td>
</tr>
<tr>
<td style="text-align:left;">
5 mins after
</td>
<td style="text-align:center;">
5
</td>
<td style="text-align:center;">
-0.063
</td>
<td style="text-align:center;">
(0.128)
</td>
<td style="text-align:center;">
[-0.314, 0.189]
</td>
<td style="text-align:center;">
0.625
</td>
<td style="text-align:center;">
41.8 / 41.8
</td>
<td style="text-align:center;">
586
</td>
<td style="text-align:center;">
66
</td>
</tr>
<tr>
<td style="text-align:left;">
15 mins after
</td>
<td style="text-align:center;">
15
</td>
<td style="text-align:center;">
0.173
</td>
<td style="text-align:center;">
(0.141)
</td>
<td style="text-align:center;">
[-0.104, 0.449]
</td>
<td style="text-align:center;">
0.220
</td>
<td style="text-align:center;">
44.9 / 44.9
</td>
<td style="text-align:center;">
628
</td>
<td style="text-align:center;">
22
</td>
</tr>
<tr>
<td style="text-align:left;">
20 mins after
</td>
<td style="text-align:center;">
20
</td>
<td style="text-align:center;">
0.106
</td>
<td style="text-align:center;">
(0.141)
</td>
<td style="text-align:center;">
[-0.170, 0.382]
</td>
<td style="text-align:center;">
0.451
</td>
<td style="text-align:center;">
48.4 / 48.4
</td>
<td style="text-align:center;">
610
</td>
<td style="text-align:center;">
23
</td>
</tr>
<tr>
<td style="text-align:left;">

</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
</tr>
<tr>
<td style="text-align:left;">
20 mins before
</td>
<td style="text-align:center;">
-20
</td>
<td style="text-align:center;">
-0.431
</td>
<td style="text-align:center;">
(5.193)
</td>
<td style="text-align:center;">
[-10.609, 9.747]
</td>
<td style="text-align:center;">
0.934
</td>
<td style="text-align:center;">
27.9 / 27.9
</td>
<td style="text-align:center;">
373
</td>
<td style="text-align:center;">
233
</td>
</tr>
<tr>
<td style="text-align:left;">
15 mins before
</td>
<td style="text-align:center;">
-15
</td>
<td style="text-align:center;">
-1.912
</td>
<td style="text-align:center;">
(7.431)
</td>
<td style="text-align:center;">
[-16.476, 12.652]
</td>
<td style="text-align:center;">
0.797
</td>
<td style="text-align:center;">
30.4 / 30.4
</td>
<td style="text-align:center;">
535
</td>
<td style="text-align:center;">
99
</td>
</tr>
<tr>
<td style="text-align:left;">
5 mins before
</td>
<td style="text-align:center;">
-5
</td>
<td style="text-align:center;">
-19.901
</td>
<td style="text-align:center;">
(34.344)
</td>
<td style="text-align:center;">
[-87.215, 47.412]
</td>
<td style="text-align:center;">
0.562
</td>
<td style="text-align:center;">
14.7 / 14.7
</td>
<td style="text-align:center;">
126
</td>
<td style="text-align:center;">
54
</td>
</tr>
<tr>
<td style="text-align:left;">
3 mins before
</td>
<td style="text-align:center;">
-3
</td>
<td style="text-align:center;">
-50.468
</td>
<td style="text-align:center;">
(55.753)
</td>
<td style="text-align:center;">
[-159.742, 58.805]
</td>
<td style="text-align:center;">
0.365
</td>
<td style="text-align:center;">
14.6 / 14.6
</td>
<td style="text-align:center;">
49
</td>
<td style="text-align:center;">
69
</td>
</tr>
<tr>
<td style="text-align:left;">
1 min before
</td>
<td style="text-align:center;">
-1
</td>
<td style="text-align:center;">
0.080
</td>
<td style="text-align:center;">
(31.266)
</td>
<td style="text-align:center;">
[-61.201, 61.360]
</td>
<td style="text-align:center;">
0.998
</td>
<td style="text-align:center;">
40.0 / 40.0
</td>
<td style="text-align:center;">
557
</td>
<td style="text-align:center;">
94
</td>
</tr>
<tr>
<td style="text-align:left;">
At cutoff
</td>
<td style="text-align:center;">
0
</td>
<td style="text-align:center;">
43.860
</td>
<td style="text-align:center;">
(46.324)
</td>
<td style="text-align:center;">
[-46.934, 134.654]
</td>
<td style="text-align:center;">
0.344
</td>
<td style="text-align:center;">
18.4 / 18.4
</td>
<td style="text-align:center;">
79
</td>
<td style="text-align:center;">
77
</td>
</tr>
<tr>
<td style="text-align:left;">
1 min after
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
0.298
</td>
<td style="text-align:center;">
(12.547)
</td>
<td style="text-align:center;">
[-24.294, 24.889]
</td>
<td style="text-align:center;">
0.981
</td>
<td style="text-align:center;">
32.3 / 32.3
</td>
<td style="text-align:center;">
563
</td>
<td style="text-align:center;">
84
</td>
</tr>
<tr>
<td style="text-align:left;">
3 mins after
</td>
<td style="text-align:center;">
3
</td>
<td style="text-align:center;">
2.240
</td>
<td style="text-align:center;">
(8.951)
</td>
<td style="text-align:center;">
[-15.304, 19.784]
</td>
<td style="text-align:center;">
0.802
</td>
<td style="text-align:center;">
36.8 / 36.8
</td>
<td style="text-align:center;">
571
</td>
<td style="text-align:center;">
80
</td>
</tr>
<tr>
<td style="text-align:left;">
5 mins after
</td>
<td style="text-align:center;">
5
</td>
<td style="text-align:center;">
5.541
</td>
<td style="text-align:center;">
(6.751)
</td>
<td style="text-align:center;">
[-7.690, 18.772]
</td>
<td style="text-align:center;">
0.412
</td>
<td style="text-align:center;">
31.3 / 31.3
</td>
<td style="text-align:center;">
497
</td>
<td style="text-align:center;">
64
</td>
</tr>
<tr>
<td style="text-align:left;">
15 mins after
</td>
<td style="text-align:center;">
15
</td>
<td style="text-align:center;">
-17.707
</td>
<td style="text-align:center;">
(10.872)
</td>
<td style="text-align:center;">
[-39.016, 3.601]
</td>
<td style="text-align:center;">
0.103
</td>
<td style="text-align:center;">
44.2 / 44.2
</td>
<td style="text-align:center;">
628
</td>
<td style="text-align:center;">
22
</td>
</tr>
<tr>
<td style="text-align:left;">
20 mins after
</td>
<td style="text-align:center;">
20
</td>
<td style="text-align:center;">
2.987
</td>
<td style="text-align:center;">
(10.136)
</td>
<td style="text-align:center;">
[-16.878, 22.852]
</td>
<td style="text-align:center;">
0.768
</td>
<td style="text-align:center;">
54.4 / 54.4
</td>
<td style="text-align:center;">
635
</td>
<td style="text-align:center;">
25
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Robust SE in parentheses.
</td>
</tr>
</tfoot>
</table>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
Pseudo cutoff
</th>
<th style="text-align:center;font-weight: bold;">
<span class="math inline">\(c\)</span> (mins)
</th>
<th style="text-align:center;font-weight: bold;">
RD estimate
</th>
<th style="text-align:center;font-weight: bold;">
SE
</th>
<th style="text-align:center;font-weight: bold;">
95% CI
</th>
<th style="text-align:center;font-weight: bold;">
<span class="math inline">\(p\)</span>
</th>
<th style="text-align:center;font-weight: bold;">
<span class="math inline">\(h_L/h_R\)</span>
</th>
<th style="text-align:center;font-weight: bold;">
<span class="math inline">\(N_L\)</span>
</th>
<th style="text-align:center;font-weight: bold;">
<span class="math inline">\(N_R\)</span>
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">

</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
</tr>
<tr>
<td style="text-align:left;">
5 mins before
</td>
<td style="text-align:center;">
-5
</td>
<td style="text-align:center;">
-0.092
</td>
<td style="text-align:center;">
(0.085)
</td>
<td style="text-align:center;">
[-0.260, 0.075]
</td>
<td style="text-align:center;">
0.279
</td>
<td style="text-align:center;">
40.0 / 40.0
</td>
<td style="text-align:center;">
53
</td>
<td style="text-align:center;">
553
</td>
</tr>
<tr>
<td style="text-align:left;">
3 mins before
</td>
<td style="text-align:center;">
-3
</td>
<td style="text-align:center;">
-0.049
</td>
<td style="text-align:center;">
(0.161)
</td>
<td style="text-align:center;">
[-0.366, 0.267]
</td>
<td style="text-align:center;">
0.759
</td>
<td style="text-align:center;">
2.6 / 2.6
</td>
<td style="text-align:center;">
110
</td>
<td style="text-align:center;">
58
</td>
</tr>
<tr>
<td style="text-align:left;">
1 min before
</td>
<td style="text-align:center;">
-1
</td>
<td style="text-align:center;">
-0.305*
</td>
<td style="text-align:center;">
(0.168)
</td>
<td style="text-align:center;">
[-0.634, 0.024]
</td>
<td style="text-align:center;">
0.070
</td>
<td style="text-align:center;">
2.5 / 2.5
</td>
<td style="text-align:center;">
100
</td>
<td style="text-align:center;">
74
</td>
</tr>
<tr>
<td style="text-align:left;">
At cutoff
</td>
<td style="text-align:center;">
0
</td>
<td style="text-align:center;">
0.305*
</td>
<td style="text-align:center;">
(0.170)
</td>
<td style="text-align:center;">
[-0.028, 0.638]
</td>
<td style="text-align:center;">
0.073
</td>
<td style="text-align:center;">
2.4 / 2.4
</td>
<td style="text-align:center;">
95
</td>
<td style="text-align:center;">
58
</td>
</tr>
<tr>
<td style="text-align:left;">
1 min after
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
-0.201
</td>
<td style="text-align:center;">
(0.180)
</td>
<td style="text-align:center;">
[-0.553, 0.151]
</td>
<td style="text-align:center;">
0.264
</td>
<td style="text-align:center;">
2.5 / 2.5
</td>
<td style="text-align:center;">
103
</td>
<td style="text-align:center;">
59
</td>
</tr>
<tr>
<td style="text-align:left;">
3 mins after
</td>
<td style="text-align:center;">
3
</td>
<td style="text-align:center;">
0.050
</td>
<td style="text-align:center;">
(0.120)
</td>
<td style="text-align:center;">
[-0.186, 0.286]
</td>
<td style="text-align:center;">
0.677
</td>
<td style="text-align:center;">
4.0 / 4.0
</td>
<td style="text-align:center;">
133
</td>
<td style="text-align:center;">
139
</td>
</tr>
<tr>
<td style="text-align:left;">
5 mins after
</td>
<td style="text-align:center;">
5
</td>
<td style="text-align:center;">
-0.057
</td>
<td style="text-align:center;">
(0.120)
</td>
<td style="text-align:center;">
[-0.292, 0.178]
</td>
<td style="text-align:center;">
0.634
</td>
<td style="text-align:center;">
4.3 / 4.3
</td>
<td style="text-align:center;">
125
</td>
<td style="text-align:center;">
104
</td>
</tr>
<tr>
<td style="text-align:left;">
15 mins after
</td>
<td style="text-align:center;">
15
</td>
<td style="text-align:center;">
-0.085
</td>
<td style="text-align:center;">
(0.187)
</td>
<td style="text-align:center;">
[-0.451, 0.282]
</td>
<td style="text-align:center;">
0.650
</td>
<td style="text-align:center;">
5.9 / 5.9
</td>
<td style="text-align:center;">
67
</td>
<td style="text-align:center;">
39
</td>
</tr>
<tr>
<td style="text-align:left;">
20 mins after
</td>
<td style="text-align:center;">
20
</td>
<td style="text-align:center;">
-0.028
</td>
<td style="text-align:center;">
(0.175)
</td>
<td style="text-align:center;">
[-0.371, 0.316]
</td>
<td style="text-align:center;">
0.874
</td>
<td style="text-align:center;">
36.0 / 36.0
</td>
<td style="text-align:center;">
597
</td>
<td style="text-align:center;">
17
</td>
</tr>
<tr>
<td style="text-align:left;">

</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
</tr>
<tr>
<td style="text-align:left;">
5 mins before
</td>
<td style="text-align:center;">
-5
</td>
<td style="text-align:center;">
-131.431
</td>
<td style="text-align:center;">
(90.078)
</td>
<td style="text-align:center;">
[-307.980, 45.118]
</td>
<td style="text-align:center;">
0.145
</td>
<td style="text-align:center;">
1.7 / 1.7
</td>
<td style="text-align:center;">
34
</td>
<td style="text-align:center;">
37
</td>
</tr>
<tr>
<td style="text-align:left;">
3 mins before
</td>
<td style="text-align:center;">
-3
</td>
<td style="text-align:center;">
-7.156
</td>
<td style="text-align:center;">
(4.569)
</td>
<td style="text-align:center;">
[-16.110, 1.799]
</td>
<td style="text-align:center;">
0.117
</td>
<td style="text-align:center;">
40.0 / 40.0
</td>
<td style="text-align:center;">
131
</td>
<td style="text-align:center;">
475
</td>
</tr>
<tr>
<td style="text-align:left;">
1 min before
</td>
<td style="text-align:center;">
-1
</td>
<td style="text-align:center;">
2.125
</td>
<td style="text-align:center;">
(5.148)
</td>
<td style="text-align:center;">
[-7.964, 12.215]
</td>
<td style="text-align:center;">
0.680
</td>
<td style="text-align:center;">
40.0 / 40.0
</td>
<td style="text-align:center;">
189
</td>
<td style="text-align:center;">
417
</td>
</tr>
<tr>
<td style="text-align:left;">
At cutoff
</td>
<td style="text-align:center;">
0
</td>
<td style="text-align:center;">
-4.296
</td>
<td style="text-align:center;">
(8.318)
</td>
<td style="text-align:center;">
[-20.600, 12.007]
</td>
<td style="text-align:center;">
0.606
</td>
<td style="text-align:center;">
4.1 / 4.1
</td>
<td style="text-align:center;">
137
</td>
<td style="text-align:center;">
124
</td>
</tr>
<tr>
<td style="text-align:left;">
1 min after
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
-2.703
</td>
<td style="text-align:center;">
(11.525)
</td>
<td style="text-align:center;">
[-25.291, 19.885]
</td>
<td style="text-align:center;">
0.815
</td>
<td style="text-align:center;">
2.8 / 2.8
</td>
<td style="text-align:center;">
103
</td>
<td style="text-align:center;">
87
</td>
</tr>
<tr>
<td style="text-align:left;">
3 mins after
</td>
<td style="text-align:center;">
3
</td>
<td style="text-align:center;">
2.266
</td>
<td style="text-align:center;">
(7.198)
</td>
<td style="text-align:center;">
[-11.842, 16.374]
</td>
<td style="text-align:center;">
0.753
</td>
<td style="text-align:center;">
4.1 / 4.1
</td>
<td style="text-align:center;">
133
</td>
<td style="text-align:center;">
139
</td>
</tr>
<tr>
<td style="text-align:left;">
5 mins after
</td>
<td style="text-align:center;">
5
</td>
<td style="text-align:center;">
2.487
</td>
<td style="text-align:center;">
(7.267)
</td>
<td style="text-align:center;">
[-11.756, 16.731]
</td>
<td style="text-align:center;">
0.732
</td>
<td style="text-align:center;">
4.0 / 4.0
</td>
<td style="text-align:center;">
125
</td>
<td style="text-align:center;">
104
</td>
</tr>
<tr>
<td style="text-align:left;">
15 mins after
</td>
<td style="text-align:center;">
15
</td>
<td style="text-align:center;">
0.729
</td>
<td style="text-align:center;">
(9.440)
</td>
<td style="text-align:center;">
[-17.774, 19.232]
</td>
<td style="text-align:center;">
0.938
</td>
<td style="text-align:center;">
5.8 / 5.8
</td>
<td style="text-align:center;">
67
</td>
<td style="text-align:center;">
39
</td>
</tr>
<tr>
<td style="text-align:left;">
20 mins after
</td>
<td style="text-align:center;">
20
</td>
<td style="text-align:center;">
2.414
</td>
<td style="text-align:center;">
(19.906)
</td>
<td style="text-align:center;">
[-36.602, 41.430]
</td>
<td style="text-align:center;">
0.903
</td>
<td style="text-align:center;">
31.8 / 31.8
</td>
<td style="text-align:center;">
597
</td>
<td style="text-align:center;">
16
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Robust SE in parentheses.
</td>
</tr>
</tfoot>
</table>
</div>
</div>
<div id="manipulation-checks" class="section level2 unnumbered">
<h2 class="unnumbered">Manipulation Checks</h2>
<div id="table-6-the-t-test-results-show-a-statistically-significant-difference-in-information-exposure-between-the-treatment-and-control-groups" class="section level3 unnumbered">
<h3 class="unnumbered">Table 6: The t-test results show a statistically
significant difference in information exposure between the treatment and
control groups</h3>
<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" tabindex="-1"></a>t_test_exposure <span class="ot">&lt;-</span> <span class="fu">t.test</span>(exposure <span class="sc">~</span> cutoff, <span class="at">data =</span> df_rdd48)</span>
<span id="cb15-2"><a href="#cb15-2" tabindex="-1"></a></span>
<span id="cb15-3"><a href="#cb15-3" tabindex="-1"></a>t_val <span class="ot">&lt;-</span> <span class="fu">unname</span>(t_test_exposure<span class="sc">$</span>statistic)</span>
<span id="cb15-4"><a href="#cb15-4" tabindex="-1"></a>p_val <span class="ot">&lt;-</span> <span class="fu">unname</span>(t_test_exposure<span class="sc">$</span>p.value)</span>
<span id="cb15-5"><a href="#cb15-5" tabindex="-1"></a>p_adj <span class="ot">&lt;-</span> <span class="fu">p.adjust</span>(p_val, <span class="at">method =</span> <span class="st">&quot;bonferroni&quot;</span>)</span>
<span id="cb15-6"><a href="#cb15-6" tabindex="-1"></a></span>
<span id="cb15-7"><a href="#cb15-7" tabindex="-1"></a>fmt_t  <span class="ot">&lt;-</span> <span class="cf">function</span>(x) <span class="fu">formatC</span>(x, <span class="at">format =</span> <span class="st">&quot;f&quot;</span>, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb15-8"><a href="#cb15-8" tabindex="-1"></a>fmt_p  <span class="ot">&lt;-</span> <span class="cf">function</span>(x) <span class="fu">ifelse</span>(x <span class="sc">&lt;</span> <span class="fl">0.001</span>, <span class="st">&quot;0.000&quot;</span>, <span class="fu">formatC</span>(x, <span class="at">format =</span> <span class="st">&quot;f&quot;</span>, <span class="at">digits =</span> <span class="dv">3</span>))</span>
<span id="cb15-9"><a href="#cb15-9" tabindex="-1"></a></span>
<span id="cb15-10"><a href="#cb15-10" tabindex="-1"></a>tab_first <span class="ot">&lt;-</span> tibble<span class="sc">::</span><span class="fu">tibble</span>(</span>
<span id="cb15-11"><a href="#cb15-11" tabindex="-1"></a>  <span class="st">`</span><span class="at"> </span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;The first gold medal&quot;</span>,</span>
<span id="cb15-12"><a href="#cb15-12" tabindex="-1"></a>  <span class="st">`</span><span class="at">t-value</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">fmt_t</span>(t_val),</span>
<span id="cb15-13"><a href="#cb15-13" tabindex="-1"></a>  <span class="st">`</span><span class="at">p-value</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">fmt_p</span>(p_val),</span>
<span id="cb15-14"><a href="#cb15-14" tabindex="-1"></a>  <span class="st">`</span><span class="at">Bonferroni adjusted p-value</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">fmt_p</span>(p_adj)</span>
<span id="cb15-15"><a href="#cb15-15" tabindex="-1"></a>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
t-value
</th>
<th style="text-align:center;font-weight: bold;">
p-value
</th>
<th style="text-align:center;font-weight: bold;">
Bonferroni adjusted p-value
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
The first gold medal
</td>
<td style="text-align:center;">
-7.373
</td>
<td style="text-align:center;">
0.000
</td>
<td style="text-align:center;">
0.000
</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1" tabindex="-1"></a>t_test_exposure <span class="ot">&lt;-</span> <span class="fu">t.test</span>(exposure <span class="sc">~</span> cutoff, <span class="at">data =</span> df_rdd66)</span>
<span id="cb16-2"><a href="#cb16-2" tabindex="-1"></a></span>
<span id="cb16-3"><a href="#cb16-3" tabindex="-1"></a>t_val <span class="ot">&lt;-</span> <span class="fu">unname</span>(t_test_exposure<span class="sc">$</span>statistic)</span>
<span id="cb16-4"><a href="#cb16-4" tabindex="-1"></a>p_val <span class="ot">&lt;-</span> <span class="fu">unname</span>(t_test_exposure<span class="sc">$</span>p.value)</span>
<span id="cb16-5"><a href="#cb16-5" tabindex="-1"></a>p_adj <span class="ot">&lt;-</span> <span class="fu">p.adjust</span>(p_val, <span class="at">method =</span> <span class="st">&quot;bonferroni&quot;</span>)</span>
<span id="cb16-6"><a href="#cb16-6" tabindex="-1"></a></span>
<span id="cb16-7"><a href="#cb16-7" tabindex="-1"></a>fmt_t  <span class="ot">&lt;-</span> <span class="cf">function</span>(x) <span class="fu">formatC</span>(x, <span class="at">format =</span> <span class="st">&quot;f&quot;</span>, <span class="at">digits =</span> <span class="dv">3</span>)</span>
<span id="cb16-8"><a href="#cb16-8" tabindex="-1"></a>fmt_p  <span class="ot">&lt;-</span> <span class="cf">function</span>(x) <span class="fu">ifelse</span>(x <span class="sc">&lt;</span> <span class="fl">0.001</span>, <span class="st">&quot;0.000&quot;</span>, <span class="fu">formatC</span>(x, <span class="at">format =</span> <span class="st">&quot;f&quot;</span>, <span class="at">digits =</span> <span class="dv">3</span>))</span>
<span id="cb16-9"><a href="#cb16-9" tabindex="-1"></a></span>
<span id="cb16-10"><a href="#cb16-10" tabindex="-1"></a>tab_second <span class="ot">&lt;-</span> tibble<span class="sc">::</span><span class="fu">tibble</span>(</span>
<span id="cb16-11"><a href="#cb16-11" tabindex="-1"></a>  <span class="st">`</span><span class="at"> </span><span class="st">`</span> <span class="ot">=</span> <span class="st">&quot;The second gold medal&quot;</span>,</span>
<span id="cb16-12"><a href="#cb16-12" tabindex="-1"></a>  <span class="st">`</span><span class="at">t-value</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">fmt_t</span>(t_val),</span>
<span id="cb16-13"><a href="#cb16-13" tabindex="-1"></a>  <span class="st">`</span><span class="at">p-value</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">fmt_p</span>(p_val),</span>
<span id="cb16-14"><a href="#cb16-14" tabindex="-1"></a>  <span class="st">`</span><span class="at">Bonferroni adjusted p-value</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">fmt_p</span>(p_adj)</span>
<span id="cb16-15"><a href="#cb16-15" tabindex="-1"></a>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
t-value
</th>
<th style="text-align:center;font-weight: bold;">
p-value
</th>
<th style="text-align:center;font-weight: bold;">
Bonferroni adjusted p-value
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
The second gold medal
</td>
<td style="text-align:center;">
-4.393
</td>
<td style="text-align:center;">
0.000
</td>
<td style="text-align:center;">
0.000
</td>
</tr>
</tbody>
</table>
</div>
<div id="table-7-the-treatment-has-a-statistically-significant-effect-on-watching-and-knowing-the-first-gold-medal-results" class="section level3 unnumbered">
<h3 class="unnumbered">Table 7: The treatment has a statistically
significant effect on watching and knowing the first gold medal
results</h3>
<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb17-1"><a href="#cb17-1" tabindex="-1"></a>m_simple <span class="ot">&lt;-</span> <span class="fu">lm</span>(exposure <span class="sc">~</span> cutoff, <span class="at">data =</span> df_rdd48)</span>
<span id="cb17-2"><a href="#cb17-2" tabindex="-1"></a>m_cov    <span class="ot">&lt;-</span> <span class="fu">lm</span>(exposure <span class="sc">~</span> cutoff <span class="sc">+</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul,</span>
<span id="cb17-3"><a href="#cb17-3" tabindex="-1"></a>               <span class="at">data =</span> df_rdd48)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
<em>Dependent variable: Exposure</em>
</div>
</th>
</tr>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<em>Simple model</em>
</th>
<th style="text-align:center;font-weight: bold;">
<em>Model with covariates</em>
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Cutoff(After=1; Before=0)
</td>
<td style="text-align:center;">
0.299***<br>(0.040)
</td>
<td style="text-align:center;">
0.316***<br>(0.046)
</td>
</tr>
<tr>
<td style="text-align:left;">
Age
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
-0.000<br>(0.001)
</td>
</tr>
<tr>
<td style="text-align:left;">
Female(Female=1; Male=0)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
-0.033<br>(0.033)
</td>
</tr>
<tr>
<td style="text-align:left;">
Education(4-scales)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
-0.001<br>(0.017)
</td>
</tr>
<tr>
<td style="text-align:left;">
Income(20-scales)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
0.001<br>(0.005)
</td>
</tr>
<tr>
<td style="text-align:left;">
Ruling party support(In-partisans=1; Otherwise=0)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
0.063<br>(0.034)
</td>
</tr>
<tr>
<td style="text-align:left;">
Constant
</td>
<td style="text-align:center;">
0.179***<br>(0.016)
</td>
<td style="text-align:center;">
0.188*<br>(0.084)
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.088
</td>
<td style="text-align:center;">
0.094
</td>
</tr>
<tr>
<td style="text-align:left;">
Adjusted <span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.086
</td>
<td style="text-align:center;">
0.086
</td>
</tr>
<tr>
<td style="text-align:left;">
Residual Std. Error
</td>
<td style="text-align:center;">
0.416
</td>
<td style="text-align:center;">
0.416
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 738)
</td>
<td style="text-align:center;">
(df = 733)
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(F\)</span> Statistic
</td>
<td style="text-align:center;">
70.932
</td>
<td style="text-align:center;">
12.626
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 1; 738)
</td>
<td style="text-align:center;">
(df = 6; 733)
</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb18-1"><a href="#cb18-1" tabindex="-1"></a>m_simple <span class="ot">&lt;-</span> <span class="fu">lm</span>(exposure <span class="sc">~</span> cutoff, <span class="at">data =</span> df_rdd66)</span>
<span id="cb18-2"><a href="#cb18-2" tabindex="-1"></a>m_cov    <span class="ot">&lt;-</span> <span class="fu">lm</span>(exposure <span class="sc">~</span> cutoff <span class="sc">+</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul,</span>
<span id="cb18-3"><a href="#cb18-3" tabindex="-1"></a>               <span class="at">data =</span> df_rdd66)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
<em>Dependent variable: Exposure</em>
</div>
</th>
</tr>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<em>Simple model</em>
</th>
<th style="text-align:center;font-weight: bold;">
<em>Model with covariates</em>
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Cutoff(After=1; Before=0)
</td>
<td style="text-align:center;">
0.155***<br>(0.035)
</td>
<td style="text-align:center;">
0.147***<br>(0.036)
</td>
</tr>
<tr>
<td style="text-align:left;">
Age
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
0.004***<br>(0.001)
</td>
</tr>
<tr>
<td style="text-align:left;">
Female(Female=1; Male=0)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
-0.004<br>(0.036)
</td>
</tr>
<tr>
<td style="text-align:left;">
Education(4-scales)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
0.002<br>(0.002)
</td>
</tr>
<tr>
<td style="text-align:left;">
Income(20-scales)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
0.011*<br>(0.005)
</td>
</tr>
<tr>
<td style="text-align:left;">
Ruling party support(In-partisans=1; Otherwise=0)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
0.061<br>(0.037)
</td>
</tr>
<tr>
<td style="text-align:left;">
Constant
</td>
<td style="text-align:center;">
0.221***<br>(0.028)
</td>
<td style="text-align:center;">
-0.084<br>(0.073)
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.024
</td>
<td style="text-align:center;">
0.048
</td>
</tr>
<tr>
<td style="text-align:left;">
Adjusted <span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.022
</td>
<td style="text-align:center;">
0.040
</td>
</tr>
<tr>
<td style="text-align:left;">
Residual Std. Error
</td>
<td style="text-align:center;">
0.464
</td>
<td style="text-align:center;">
0.460
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 710)
</td>
<td style="text-align:center;">
(df = 705)
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(F\)</span> Statistic
</td>
<td style="text-align:center;">
17.263
</td>
<td style="text-align:center;">
5.919
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 1; 710)
</td>
<td style="text-align:center;">
(df = 6; 705)
</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
<div id="code-for-the-supplementary-information" class="section level1 unnumbered">
<h1 class="unnumbered">Code for the Supplementary Information</h1>
<div id="c.-full-equivalence-test-results-feeling-thermometer" class="section level2 unnumbered">
<h2 class="unnumbered">C. Full Equivalence-Test Results: Feeling
Thermometer</h2>
<div id="figure-c1-for-the-first-gold-medal-cross-sectional-equivalence-is-missed-at-k-0.2-but-holds-from-k-0.3-upward-and-time-bin-equivalence-appears-mainly-at-k-1.0-with-narrow-bins-implying-at-most-a-small-effect-on-the-feeling-thermometer" class="section level3 unnumbered">
<h3 class="unnumbered">Figure C1: For the first gold medal,
cross-sectional equivalence is missed at k = 0.2 but holds from k = 0.3
upward, and time-bin equivalence appears mainly at k = 1.0 with narrow
bins, implying at most a small effect on the feeling thermometer</h3>
<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1" tabindex="-1"></a><span class="cf">if</span> (<span class="fu">exists</span>(<span class="st">&quot;df_rdd48&quot;</span>)) {</span>
<span id="cb19-2"><a href="#cb19-2" tabindex="-1"></a></span>
<span id="cb19-3"><a href="#cb19-3" tabindex="-1"></a>  cs_mode_use <span class="ot">&lt;-</span> <span class="st">&quot;pooled&quot;</span></span>
<span id="cb19-4"><a href="#cb19-4" tabindex="-1"></a>  bin_set_use <span class="ot">&lt;-</span> <span class="fu">seq</span>(<span class="dv">10</span>, <span class="dv">120</span>, <span class="at">by =</span> <span class="dv">10</span>)    </span>
<span id="cb19-5"><a href="#cb19-5" tabindex="-1"></a>  k_set_use   <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.5</span>, <span class="fl">1.0</span>)</span>
<span id="cb19-6"><a href="#cb19-6" tabindex="-1"></a></span>
<span id="cb19-7"><a href="#cb19-7" tabindex="-1"></a>  tab_ftj <span class="ot">&lt;-</span> <span class="fu">build_overlap_singlecs</span>(</span>
<span id="cb19-8"><a href="#cb19-8" tabindex="-1"></a>    <span class="at">df        =</span> df_rdd48,</span>
<span id="cb19-9"><a href="#cb19-9" tabindex="-1"></a>    <span class="at">outcome   =</span> <span class="st">&quot;ftj&quot;</span>,</span>
<span id="cb19-10"><a href="#cb19-10" tabindex="-1"></a>    <span class="at">time_col  =</span> <span class="st">&quot;running_var&quot;</span>,</span>
<span id="cb19-11"><a href="#cb19-11" tabindex="-1"></a>    <span class="at">bin_set   =</span> bin_set_use,</span>
<span id="cb19-12"><a href="#cb19-12" tabindex="-1"></a>    <span class="at">k_set     =</span> k_set_use,</span>
<span id="cb19-13"><a href="#cb19-13" tabindex="-1"></a>    <span class="at">cs_mode   =</span> cs_mode_use,</span>
<span id="cb19-14"><a href="#cb19-14" tabindex="-1"></a>    <span class="at">unit_time =</span> <span class="st">&quot;mins&quot;</span></span>
<span id="cb19-15"><a href="#cb19-15" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
<span id="cb19-16"><a href="#cb19-16" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb19-17"><a href="#cb19-17" tabindex="-1"></a>      <span class="at">EB_TB_low  =</span> <span class="sc">-</span>eps_tb,</span>
<span id="cb19-18"><a href="#cb19-18" tabindex="-1"></a>      <span class="at">EB_TB_high =</span>  eps_tb,</span>
<span id="cb19-19"><a href="#cb19-19" tabindex="-1"></a>      <span class="at">EB_CS_low  =</span> <span class="sc">-</span>eps_cs,</span>
<span id="cb19-20"><a href="#cb19-20" tabindex="-1"></a>      <span class="at">EB_CS_high =</span>  eps_cs</span>
<span id="cb19-21"><a href="#cb19-21" tabindex="-1"></a>    )</span>
<span id="cb19-22"><a href="#cb19-22" tabindex="-1"></a></span>
<span id="cb19-23"><a href="#cb19-23" tabindex="-1"></a>  p_ftj <span class="ot">&lt;-</span> <span class="fu">plot_overlap_blue_EB</span>(</span>
<span id="cb19-24"><a href="#cb19-24" tabindex="-1"></a>    tab_ftj,</span>
<span id="cb19-25"><a href="#cb19-25" tabindex="-1"></a>    <span class="fu">common_ci90</span>(df_rdd48, <span class="st">&quot;ftj&quot;</span>),</span>
<span id="cb19-26"><a href="#cb19-26" tabindex="-1"></a>    <span class="at">title   =</span> <span class="st">&quot;Feeling thermometer: overlap of equivalence (1st gold medal)&quot;</span>,</span>
<span id="cb19-27"><a href="#cb19-27" tabindex="-1"></a>    <span class="at">cs_mode =</span> cs_mode_use</span>
<span id="cb19-28"><a href="#cb19-28" tabindex="-1"></a>  )</span>
<span id="cb19-29"><a href="#cb19-29" tabindex="-1"></a></span>
<span id="cb19-30"><a href="#cb19-30" tabindex="-1"></a>  <span class="fu">print</span>(p_ftj)</span>
<span id="cb19-31"><a href="#cb19-31" tabindex="-1"></a></span>
<span id="cb19-32"><a href="#cb19-32" tabindex="-1"></a>  tab_bounds_ftj <span class="ot">&lt;-</span> tab_ftj <span class="sc">%&gt;%</span></span>
<span id="cb19-33"><a href="#cb19-33" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">select</span>(bin_minutes, k, category,</span>
<span id="cb19-34"><a href="#cb19-34" tabindex="-1"></a>                  EB_TB_low, EB_TB_high, EB_CS_low, EB_CS_high) <span class="sc">%&gt;%</span></span>
<span id="cb19-35"><a href="#cb19-35" tabindex="-1"></a>    dplyr<span class="sc">::</span><span class="fu">arrange</span>(k, bin_minutes)</span>
<span id="cb19-36"><a href="#cb19-36" tabindex="-1"></a></span>
<span id="cb19-37"><a href="#cb19-37" tabindex="-1"></a>  knitr<span class="sc">::</span><span class="fu">kable</span>(</span>
<span id="cb19-38"><a href="#cb19-38" tabindex="-1"></a>    tab_bounds_ftj,</span>
<span id="cb19-39"><a href="#cb19-39" tabindex="-1"></a>    <span class="at">digits  =</span> <span class="dv">3</span>,</span>
<span id="cb19-40"><a href="#cb19-40" tabindex="-1"></a>    <span class="at">caption =</span> <span class="st">&quot;Equivalence bounds by bin width and k (feeling thermometer, 1st gold medal)&quot;</span></span>
<span id="cb19-41"><a href="#cb19-41" tabindex="-1"></a>  )</span>
<span id="cb19-42"><a href="#cb19-42" tabindex="-1"></a>}</span></code></pre></div>
<p><img role="img" 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" width="672" /></p>
<table>
<caption>Equivalence bounds by bin width and k (feeling thermometer, 1st
gold medal)</caption>
<colgroup>
<col width="17%" />
<col width="5%" />
<col width="13%" />
<col width="14%" />
<col width="16%" />
<col width="14%" />
<col width="16%" />
</colgroup>
<thead>
<tr class="header">
<th align="right">bin_minutes</th>
<th align="right">k</th>
<th align="left">category</th>
<th align="right">EB_TB_low</th>
<th align="right">EB_TB_high</th>
<th align="right">EB_CS_low</th>
<th align="right">EB_CS_high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-3.987</td>
<td align="right">3.987</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-2.140</td>
<td align="right">2.140</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-2.320</td>
<td align="right">2.320</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-1.578</td>
<td align="right">1.578</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.428</td>
<td align="right">0.428</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-1.391</td>
<td align="right">1.391</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-1.427</td>
<td align="right">1.427</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.721</td>
<td align="right">0.721</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.527</td>
<td align="right">0.527</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.297</td>
<td align="right">0.297</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-1.527</td>
<td align="right">1.527</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.2</td>
<td align="left">Neither</td>
<td align="right">-0.427</td>
<td align="right">0.427</td>
<td align="right">-5.224</td>
<td align="right">5.224</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-5.981</td>
<td align="right">5.981</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-3.210</td>
<td align="right">3.210</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-3.480</td>
<td align="right">3.480</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-2.366</td>
<td align="right">2.366</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.642</td>
<td align="right">0.642</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-2.086</td>
<td align="right">2.086</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-2.140</td>
<td align="right">2.140</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-1.081</td>
<td align="right">1.081</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.791</td>
<td align="right">0.791</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.446</td>
<td align="right">0.446</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-2.291</td>
<td align="right">2.291</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-0.641</td>
<td align="right">0.641</td>
<td align="right">-7.836</td>
<td align="right">7.836</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-9.968</td>
<td align="right">9.968</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-5.351</td>
<td align="right">5.351</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-5.800</td>
<td align="right">5.800</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-3.944</td>
<td align="right">3.944</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-1.071</td>
<td align="right">1.071</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-3.477</td>
<td align="right">3.477</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-3.567</td>
<td align="right">3.567</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-1.802</td>
<td align="right">1.802</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-1.319</td>
<td align="right">1.319</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-0.744</td>
<td align="right">0.744</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-3.818</td>
<td align="right">3.818</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-1.068</td>
<td align="right">1.068</td>
<td align="right">-13.060</td>
<td align="right">13.060</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-19.936</td>
<td align="right">19.936</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-10.701</td>
<td align="right">10.701</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-11.600</td>
<td align="right">11.600</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-7.888</td>
<td align="right">7.888</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-2.141</td>
<td align="right">2.141</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-6.955</td>
<td align="right">6.955</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-7.134</td>
<td align="right">7.134</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-3.603</td>
<td align="right">3.603</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-2.637</td>
<td align="right">2.637</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-1.487</td>
<td align="right">1.487</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-7.637</td>
<td align="right">7.637</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-2.136</td>
<td align="right">2.136</td>
<td align="right">-26.121</td>
<td align="right">26.121</td>
</tr>
</tbody>
</table>
</div>
<div id="figure-c2-for-the-second-gold-medal-cross-sectional-equivalence-is-missed-at-k-0.2-but-holds-from-k-0.3-upward-and-time-bin-equivalence-appears-mainly-at-k-1.0-with-narrow-bins-implying-at-most-a-small-effect-on-the-feeling-thermometer" class="section level3 unnumbered">
<h3 class="unnumbered">Figure C2: For the second gold medal,
cross-sectional equivalence is missed at k = 0.2 but holds from k = 0.3
upward, and time-bin equivalence appears mainly at k = 1.0 with narrow
bins, implying at most a small effect on the feeling thermometer</h3>
<p><img role="img" 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" width="672" /></p>
<table>
<caption>Equivalence bounds by bin width and k (feeling thermometer, 2nd
gold medal)</caption>
<colgroup>
<col width="17%" />
<col width="5%" />
<col width="13%" />
<col width="14%" />
<col width="16%" />
<col width="14%" />
<col width="16%" />
</colgroup>
<thead>
<tr class="header">
<th align="right">bin_minutes</th>
<th align="right">k</th>
<th align="left">category</th>
<th align="right">EB_TB_low</th>
<th align="right">EB_TB_high</th>
<th align="right">EB_CS_low</th>
<th align="right">EB_CS_high</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-3.028</td>
<td align="right">3.028</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-2.511</td>
<td align="right">2.511</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-2.310</td>
<td align="right">2.310</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-1.769</td>
<td align="right">1.769</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-1.578</td>
<td align="right">1.578</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-2.399</td>
<td align="right">2.399</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-1.939</td>
<td align="right">1.939</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-1.480</td>
<td align="right">1.480</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.672</td>
<td align="right">0.672</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-1.571</td>
<td align="right">1.571</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-1.755</td>
<td align="right">1.755</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.2</td>
<td align="left">CS only</td>
<td align="right">-0.777</td>
<td align="right">0.777</td>
<td align="right">-5.055</td>
<td align="right">5.055</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.3</td>
<td align="left">Both</td>
<td align="right">-4.542</td>
<td align="right">4.542</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.3</td>
<td align="left">Both</td>
<td align="right">-3.766</td>
<td align="right">3.766</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.3</td>
<td align="left">Both</td>
<td align="right">-3.464</td>
<td align="right">3.464</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-2.653</td>
<td align="right">2.653</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-2.367</td>
<td align="right">2.367</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.3</td>
<td align="left">Both</td>
<td align="right">-3.598</td>
<td align="right">3.598</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-2.908</td>
<td align="right">2.908</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-2.221</td>
<td align="right">2.221</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-1.007</td>
<td align="right">1.007</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-2.356</td>
<td align="right">2.356</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-2.632</td>
<td align="right">2.632</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.3</td>
<td align="left">CS only</td>
<td align="right">-1.166</td>
<td align="right">1.166</td>
<td align="right">-7.582</td>
<td align="right">7.582</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-7.570</td>
<td align="right">7.570</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-6.277</td>
<td align="right">6.277</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-5.774</td>
<td align="right">5.774</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-4.422</td>
<td align="right">4.422</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-3.945</td>
<td align="right">3.945</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-5.997</td>
<td align="right">5.997</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-4.847</td>
<td align="right">4.847</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-3.701</td>
<td align="right">3.701</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-1.679</td>
<td align="right">1.679</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-3.927</td>
<td align="right">3.927</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">0.5</td>
<td align="left">Both</td>
<td align="right">-4.387</td>
<td align="right">4.387</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">0.5</td>
<td align="left">CS only</td>
<td align="right">-1.943</td>
<td align="right">1.943</td>
<td align="right">-12.637</td>
<td align="right">12.637</td>
</tr>
<tr class="odd">
<td align="right">10</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-15.140</td>
<td align="right">15.140</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="even">
<td align="right">20</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-12.555</td>
<td align="right">12.555</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="odd">
<td align="right">30</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-11.548</td>
<td align="right">11.548</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="even">
<td align="right">40</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-8.843</td>
<td align="right">8.843</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="odd">
<td align="right">50</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-7.889</td>
<td align="right">7.889</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="even">
<td align="right">60</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-11.994</td>
<td align="right">11.994</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="odd">
<td align="right">70</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-9.693</td>
<td align="right">9.693</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="even">
<td align="right">80</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-7.402</td>
<td align="right">7.402</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="odd">
<td align="right">90</td>
<td align="right">1.0</td>
<td align="left">CS only</td>
<td align="right">-3.358</td>
<td align="right">3.358</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="even">
<td align="right">100</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-7.855</td>
<td align="right">7.855</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="odd">
<td align="right">110</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-8.774</td>
<td align="right">8.774</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
<tr class="even">
<td align="right">120</td>
<td align="right">1.0</td>
<td align="left">Both</td>
<td align="right">-3.886</td>
<td align="right">3.886</td>
<td align="right">-25.274</td>
<td align="right">25.274</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="d.-robustness-checks-with-unexpected-events-during-survey-design-approach" class="section level2 unnumbered">
<h2 class="unnumbered">D. Robustness checks with unexpected events
during survey design approach</h2>
</div>
<div id="d.1-demographic-issues" class="section level2 unnumbered">
<h2 class="unnumbered">D.1 Demographic issues</h2>
<div id="figure-d1-comparison-with-national-demography-1st-gold-medal" class="section level3 unnumbered">
<h3 class="unnumbered">Figure D1: Comparison with national demography
(1st gold medal)</h3>
<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1" tabindex="-1"></a>targets_judo <span class="ot">&lt;-</span> tibble<span class="sc">::</span><span class="fu">tribble</span>(</span>
<span id="cb20-2"><a href="#cb20-2" tabindex="-1"></a>  <span class="sc">~</span>indicator,   <span class="sc">~</span>label,                         <span class="sc">~</span>target,</span>
<span id="cb20-3"><a href="#cb20-3" tabindex="-1"></a>  <span class="co"># gender</span></span>
<span id="cb20-4"><a href="#cb20-4" tabindex="-1"></a>  <span class="st">&quot;gender&quot;</span>,     <span class="st">&quot;Female&quot;</span>,                       <span class="fl">0.514</span>,</span>
<span id="cb20-5"><a href="#cb20-5" tabindex="-1"></a>  <span class="st">&quot;gender&quot;</span>,     <span class="st">&quot;Male&quot;</span>,                         <span class="dv">1</span> <span class="sc">-</span> <span class="fl">0.514</span>,</span>
<span id="cb20-6"><a href="#cb20-6" tabindex="-1"></a>  </span>
<span id="cb20-7"><a href="#cb20-7" tabindex="-1"></a>  <span class="co"># age </span></span>
<span id="cb20-8"><a href="#cb20-8" tabindex="-1"></a>  <span class="st">&quot;age&quot;</span>,        <span class="st">&quot;18–34&quot;</span>,                        <span class="fl">0.172</span>,</span>
<span id="cb20-9"><a href="#cb20-9" tabindex="-1"></a>  <span class="st">&quot;age&quot;</span>,        <span class="st">&quot;35–54&quot;</span>,                        <span class="fl">0.270</span>,</span>
<span id="cb20-10"><a href="#cb20-10" tabindex="-1"></a>  <span class="st">&quot;age&quot;</span>,        <span class="st">&quot;55+&quot;</span>,                          <span class="fl">0.418</span>,</span>
<span id="cb20-11"><a href="#cb20-11" tabindex="-1"></a>  </span>
<span id="cb20-12"><a href="#cb20-12" tabindex="-1"></a>  <span class="co"># education</span></span>
<span id="cb20-13"><a href="#cb20-13" tabindex="-1"></a>  <span class="st">&quot;education&quot;</span>,  <span class="st">&quot;With a university degree&quot;</span>,     <span class="fl">0.26</span>,</span>
<span id="cb20-14"><a href="#cb20-14" tabindex="-1"></a>  <span class="st">&quot;education&quot;</span>,  <span class="st">&quot;Without a university degree&quot;</span>,  <span class="dv">1</span> <span class="sc">-</span> <span class="fl">0.26</span></span>
<span id="cb20-15"><a href="#cb20-15" tabindex="-1"></a>)</span>
<span id="cb20-16"><a href="#cb20-16" tabindex="-1"></a></span>
<span id="cb20-17"><a href="#cb20-17" tabindex="-1"></a></span>
<span id="cb20-18"><a href="#cb20-18" tabindex="-1"></a>df_demo <span class="ot">&lt;-</span> df_rdd48 <span class="sc">%&gt;%</span></span>
<span id="cb20-19"><a href="#cb20-19" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb20-20"><a href="#cb20-20" tabindex="-1"></a>    <span class="at">age_band =</span> <span class="fu">case_when</span>(</span>
<span id="cb20-21"><a href="#cb20-21" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">18</span> <span class="sc">&amp;</span> age <span class="sc">&lt;=</span> <span class="dv">34</span> <span class="sc">~</span> <span class="st">&quot;18–34&quot;</span>,</span>
<span id="cb20-22"><a href="#cb20-22" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">35</span> <span class="sc">&amp;</span> age <span class="sc">&lt;=</span> <span class="dv">54</span> <span class="sc">~</span> <span class="st">&quot;35–54&quot;</span>,</span>
<span id="cb20-23"><a href="#cb20-23" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">55</span>             <span class="sc">~</span> <span class="st">&quot;55+&quot;</span>,</span>
<span id="cb20-24"><a href="#cb20-24" tabindex="-1"></a>      <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb20-25"><a href="#cb20-25" tabindex="-1"></a>    ),</span>
<span id="cb20-26"><a href="#cb20-26" tabindex="-1"></a>    </span>
<span id="cb20-27"><a href="#cb20-27" tabindex="-1"></a>    <span class="at">gender_lab =</span> <span class="fu">case_when</span>(</span>
<span id="cb20-28"><a href="#cb20-28" tabindex="-1"></a>      female <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>) <span class="sc">~</span> <span class="fu">if_else</span>(female <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb20-29"><a href="#cb20-29" tabindex="-1"></a>      <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb20-30"><a href="#cb20-30" tabindex="-1"></a>    ),</span>
<span id="cb20-31"><a href="#cb20-31" tabindex="-1"></a>    </span>
<span id="cb20-32"><a href="#cb20-32" tabindex="-1"></a>    <span class="at">edu_lab =</span> <span class="fu">case_when</span>(</span>
<span id="cb20-33"><a href="#cb20-33" tabindex="-1"></a>      education <span class="sc">==</span> <span class="dv">4</span>              <span class="sc">~</span> <span class="st">&quot;With a university degree&quot;</span>,</span>
<span id="cb20-34"><a href="#cb20-34" tabindex="-1"></a>      education <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>)   <span class="sc">~</span> <span class="st">&quot;Without a university degree&quot;</span>,</span>
<span id="cb20-35"><a href="#cb20-35" tabindex="-1"></a>      <span class="cn">TRUE</span>                        <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb20-36"><a href="#cb20-36" tabindex="-1"></a>    )</span>
<span id="cb20-37"><a href="#cb20-37" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
<span id="cb20-38"><a href="#cb20-38" tabindex="-1"></a>  <span class="fu">filter</span>(</span>
<span id="cb20-39"><a href="#cb20-39" tabindex="-1"></a>    <span class="sc">!</span><span class="fu">is.na</span>(age_band),</span>
<span id="cb20-40"><a href="#cb20-40" tabindex="-1"></a>    <span class="sc">!</span><span class="fu">is.na</span>(gender_lab),</span>
<span id="cb20-41"><a href="#cb20-41" tabindex="-1"></a>    <span class="sc">!</span><span class="fu">is.na</span>(edu_lab)</span>
<span id="cb20-42"><a href="#cb20-42" tabindex="-1"></a>  )</span>
<span id="cb20-43"><a href="#cb20-43" tabindex="-1"></a></span>
<span id="cb20-44"><a href="#cb20-44" tabindex="-1"></a></span>
<span id="cb20-45"><a href="#cb20-45" tabindex="-1"></a></span>
<span id="cb20-46"><a href="#cb20-46" tabindex="-1"></a>sample_gender <span class="ot">&lt;-</span> df_demo <span class="sc">%&gt;%</span></span>
<span id="cb20-47"><a href="#cb20-47" tabindex="-1"></a>  <span class="fu">count</span>(gender_lab) <span class="sc">%&gt;%</span></span>
<span id="cb20-48"><a href="#cb20-48" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample =</span> n <span class="sc">/</span> <span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span></span>
<span id="cb20-49"><a href="#cb20-49" tabindex="-1"></a>  <span class="fu">transmute</span>(</span>
<span id="cb20-50"><a href="#cb20-50" tabindex="-1"></a>    <span class="at">indicator =</span> <span class="st">&quot;gender&quot;</span>,</span>
<span id="cb20-51"><a href="#cb20-51" tabindex="-1"></a>    <span class="at">label     =</span> gender_lab,</span>
<span id="cb20-52"><a href="#cb20-52" tabindex="-1"></a>    sample</span>
<span id="cb20-53"><a href="#cb20-53" tabindex="-1"></a>  )</span>
<span id="cb20-54"><a href="#cb20-54" tabindex="-1"></a></span>
<span id="cb20-55"><a href="#cb20-55" tabindex="-1"></a>sample_age <span class="ot">&lt;-</span> df_demo <span class="sc">%&gt;%</span></span>
<span id="cb20-56"><a href="#cb20-56" tabindex="-1"></a>  <span class="fu">count</span>(age_band) <span class="sc">%&gt;%</span></span>
<span id="cb20-57"><a href="#cb20-57" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample =</span> n <span class="sc">/</span> <span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span></span>
<span id="cb20-58"><a href="#cb20-58" tabindex="-1"></a>  <span class="fu">transmute</span>(</span>
<span id="cb20-59"><a href="#cb20-59" tabindex="-1"></a>    <span class="at">indicator =</span> <span class="st">&quot;age&quot;</span>,</span>
<span id="cb20-60"><a href="#cb20-60" tabindex="-1"></a>    <span class="at">label     =</span> age_band,</span>
<span id="cb20-61"><a href="#cb20-61" tabindex="-1"></a>    sample</span>
<span id="cb20-62"><a href="#cb20-62" tabindex="-1"></a>  )</span>
<span id="cb20-63"><a href="#cb20-63" tabindex="-1"></a></span>
<span id="cb20-64"><a href="#cb20-64" tabindex="-1"></a>sample_edu <span class="ot">&lt;-</span> df_demo <span class="sc">%&gt;%</span></span>
<span id="cb20-65"><a href="#cb20-65" tabindex="-1"></a>  <span class="fu">count</span>(edu_lab) <span class="sc">%&gt;%</span></span>
<span id="cb20-66"><a href="#cb20-66" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample =</span> n <span class="sc">/</span> <span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span></span>
<span id="cb20-67"><a href="#cb20-67" tabindex="-1"></a>  <span class="fu">transmute</span>(</span>
<span id="cb20-68"><a href="#cb20-68" tabindex="-1"></a>    <span class="at">indicator =</span> <span class="st">&quot;education&quot;</span>,</span>
<span id="cb20-69"><a href="#cb20-69" tabindex="-1"></a>    <span class="at">label     =</span> edu_lab,</span>
<span id="cb20-70"><a href="#cb20-70" tabindex="-1"></a>    sample</span>
<span id="cb20-71"><a href="#cb20-71" tabindex="-1"></a>  )</span>
<span id="cb20-72"><a href="#cb20-72" tabindex="-1"></a></span>
<span id="cb20-73"><a href="#cb20-73" tabindex="-1"></a>sample_all <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(sample_gender, sample_age, sample_edu)</span>
<span id="cb20-74"><a href="#cb20-74" tabindex="-1"></a></span>
<span id="cb20-75"><a href="#cb20-75" tabindex="-1"></a></span>
<span id="cb20-76"><a href="#cb20-76" tabindex="-1"></a>comp <span class="ot">&lt;-</span> targets_judo <span class="sc">%&gt;%</span></span>
<span id="cb20-77"><a href="#cb20-77" tabindex="-1"></a>  <span class="fu">left_join</span>(sample_all, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;indicator&quot;</span>, <span class="st">&quot;label&quot;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb20-78"><a href="#cb20-78" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb20-79"><a href="#cb20-79" tabindex="-1"></a>    <span class="at">Target =</span> target,</span>
<span id="cb20-80"><a href="#cb20-80" tabindex="-1"></a>    <span class="at">Sample =</span> sample,</span>
<span id="cb20-81"><a href="#cb20-81" tabindex="-1"></a>    <span class="at">label_plot =</span> label</span>
<span id="cb20-82"><a href="#cb20-82" tabindex="-1"></a>  )</span>
<span id="cb20-83"><a href="#cb20-83" tabindex="-1"></a></span>
<span id="cb20-84"><a href="#cb20-84" tabindex="-1"></a>p_demo <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(comp, <span class="fu">aes</span>(<span class="at">x =</span> Target, <span class="at">y =</span> Sample)) <span class="sc">+</span></span>
<span id="cb20-85"><a href="#cb20-85" tabindex="-1"></a>  <span class="fu">geom_abline</span>(<span class="at">slope =</span> <span class="dv">1</span>, <span class="at">intercept =</span> <span class="dv">0</span>,</span>
<span id="cb20-86"><a href="#cb20-86" tabindex="-1"></a>              <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="at">color =</span> <span class="st">&quot;grey60&quot;</span>) <span class="sc">+</span></span>
<span id="cb20-87"><a href="#cb20-87" tabindex="-1"></a>  <span class="fu">geom_segment</span>(</span>
<span id="cb20-88"><a href="#cb20-88" tabindex="-1"></a>    <span class="fu">aes</span>(<span class="at">x =</span> Target, <span class="at">xend =</span> Target,</span>
<span id="cb20-89"><a href="#cb20-89" tabindex="-1"></a>        <span class="at">y =</span> Target, <span class="at">yend =</span> Sample,</span>
<span id="cb20-90"><a href="#cb20-90" tabindex="-1"></a>        <span class="at">group =</span> <span class="fu">interaction</span>(indicator, label_plot)),</span>
<span id="cb20-91"><a href="#cb20-91" tabindex="-1"></a>    <span class="at">linewidth =</span> <span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.6</span>,</span>
<span id="cb20-92"><a href="#cb20-92" tabindex="-1"></a>    <span class="at">color =</span> <span class="st">&quot;grey50&quot;</span>, <span class="at">linetype =</span> <span class="st">&quot;dotted&quot;</span></span>
<span id="cb20-93"><a href="#cb20-93" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb20-94"><a href="#cb20-94" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">color =</span> indicator), <span class="at">size =</span> <span class="fl">3.5</span>) <span class="sc">+</span></span>
<span id="cb20-95"><a href="#cb20-95" tabindex="-1"></a>  ggrepel<span class="sc">::</span><span class="fu">geom_text_repel</span>(</span>
<span id="cb20-96"><a href="#cb20-96" tabindex="-1"></a>    <span class="fu">aes</span>(<span class="at">label =</span> label_plot, <span class="at">color =</span> indicator),</span>
<span id="cb20-97"><a href="#cb20-97" tabindex="-1"></a>    <span class="at">size =</span> <span class="dv">4</span>, <span class="at">seed =</span> <span class="dv">2024</span>, <span class="at">min.segment.length =</span> <span class="fl">0.1</span>,</span>
<span id="cb20-98"><a href="#cb20-98" tabindex="-1"></a>    <span class="at">box.padding =</span> <span class="fl">0.25</span>, <span class="at">point.padding =</span> <span class="fl">0.25</span>,</span>
<span id="cb20-99"><a href="#cb20-99" tabindex="-1"></a>    <span class="at">show.legend =</span> <span class="cn">FALSE</span></span>
<span id="cb20-100"><a href="#cb20-100" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb20-101"><a href="#cb20-101" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(</span>
<span id="cb20-102"><a href="#cb20-102" tabindex="-1"></a>    <span class="at">labels =</span> scales<span class="sc">::</span><span class="fu">percent_format</span>(<span class="at">accuracy =</span> <span class="dv">1</span>),</span>
<span id="cb20-103"><a href="#cb20-103" tabindex="-1"></a>    <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="fu">max</span>(comp<span class="sc">$</span>Target, comp<span class="sc">$</span>Sample, <span class="at">na.rm =</span> <span class="cn">TRUE</span>) <span class="sc">*</span> <span class="fl">1.05</span>)</span>
<span id="cb20-104"><a href="#cb20-104" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb20-105"><a href="#cb20-105" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(</span>
<span id="cb20-106"><a href="#cb20-106" tabindex="-1"></a>    <span class="at">labels =</span> scales<span class="sc">::</span><span class="fu">percent_format</span>(<span class="at">accuracy =</span> <span class="dv">1</span>),</span>
<span id="cb20-107"><a href="#cb20-107" tabindex="-1"></a>    <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="fu">max</span>(comp<span class="sc">$</span>Target, comp<span class="sc">$</span>Sample, <span class="at">na.rm =</span> <span class="cn">TRUE</span>) <span class="sc">*</span> <span class="fl">1.05</span>)</span>
<span id="cb20-108"><a href="#cb20-108" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb20-109"><a href="#cb20-109" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(</span>
<span id="cb20-110"><a href="#cb20-110" tabindex="-1"></a>    <span class="at">values =</span> <span class="fu">c</span>(</span>
<span id="cb20-111"><a href="#cb20-111" tabindex="-1"></a>      <span class="at">gender    =</span> <span class="st">&quot;steelblue&quot;</span>,</span>
<span id="cb20-112"><a href="#cb20-112" tabindex="-1"></a>      <span class="at">age       =</span> <span class="st">&quot;seagreen&quot;</span>,</span>
<span id="cb20-113"><a href="#cb20-113" tabindex="-1"></a>      <span class="at">education =</span> <span class="st">&quot;goldenrod&quot;</span></span>
<span id="cb20-114"><a href="#cb20-114" tabindex="-1"></a>    )</span>
<span id="cb20-115"><a href="#cb20-115" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb20-116"><a href="#cb20-116" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb20-117"><a href="#cb20-117" tabindex="-1"></a>    <span class="at">x =</span> <span class="st">&quot;Target (national)&quot;</span>,</span>
<span id="cb20-118"><a href="#cb20-118" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Sample (1st gold medal RDD sample)&quot;</span></span>
<span id="cb20-119"><a href="#cb20-119" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb20-120"><a href="#cb20-120" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb20-121"><a href="#cb20-121" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb20-122"><a href="#cb20-122" tabindex="-1"></a>    <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb20-123"><a href="#cb20-123" tabindex="-1"></a>    <span class="at">legend.position  =</span> <span class="st">&quot;none&quot;</span></span>
<span id="cb20-124"><a href="#cb20-124" tabindex="-1"></a>  )</span>
<span id="cb20-125"><a href="#cb20-125" tabindex="-1"></a></span>
<span id="cb20-126"><a href="#cb20-126" tabindex="-1"></a><span class="fu">print</span>(p_demo)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
</div>
<div id="figure-d1-comparison-with-national-demography-2nd-gold-medal" class="section level3 unnumbered">
<h3 class="unnumbered">Figure D1: Comparison with national demography
(2nd gold medal)</h3>
<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb21-1"><a href="#cb21-1" tabindex="-1"></a>targets_judo <span class="ot">&lt;-</span> tibble<span class="sc">::</span><span class="fu">tribble</span>(</span>
<span id="cb21-2"><a href="#cb21-2" tabindex="-1"></a>  <span class="sc">~</span>indicator,   <span class="sc">~</span>label,                         <span class="sc">~</span>target,</span>
<span id="cb21-3"><a href="#cb21-3" tabindex="-1"></a>  <span class="st">&quot;gender&quot;</span>,     <span class="st">&quot;Female&quot;</span>,                       <span class="fl">0.514</span>,</span>
<span id="cb21-4"><a href="#cb21-4" tabindex="-1"></a>  <span class="st">&quot;gender&quot;</span>,     <span class="st">&quot;Male&quot;</span>,                         <span class="dv">1</span> <span class="sc">-</span> <span class="fl">0.514</span>,</span>
<span id="cb21-5"><a href="#cb21-5" tabindex="-1"></a>  </span>
<span id="cb21-6"><a href="#cb21-6" tabindex="-1"></a>  <span class="st">&quot;age&quot;</span>,        <span class="st">&quot;18–34&quot;</span>,                        <span class="fl">0.172</span>,</span>
<span id="cb21-7"><a href="#cb21-7" tabindex="-1"></a>  <span class="st">&quot;age&quot;</span>,        <span class="st">&quot;35–54&quot;</span>,                        <span class="fl">0.270</span>,</span>
<span id="cb21-8"><a href="#cb21-8" tabindex="-1"></a>  <span class="st">&quot;age&quot;</span>,        <span class="st">&quot;55+&quot;</span>,                          <span class="fl">0.418</span>,</span>
<span id="cb21-9"><a href="#cb21-9" tabindex="-1"></a>  </span>
<span id="cb21-10"><a href="#cb21-10" tabindex="-1"></a>  <span class="st">&quot;education&quot;</span>,  <span class="st">&quot;With a university degree&quot;</span>,     <span class="fl">0.26</span>,</span>
<span id="cb21-11"><a href="#cb21-11" tabindex="-1"></a>  <span class="st">&quot;education&quot;</span>,  <span class="st">&quot;Without a university degree&quot;</span>,  <span class="dv">1</span> <span class="sc">-</span> <span class="fl">0.26</span></span>
<span id="cb21-12"><a href="#cb21-12" tabindex="-1"></a>)</span>
<span id="cb21-13"><a href="#cb21-13" tabindex="-1"></a></span>
<span id="cb21-14"><a href="#cb21-14" tabindex="-1"></a></span>
<span id="cb21-15"><a href="#cb21-15" tabindex="-1"></a>df_demo <span class="ot">&lt;-</span> df_rdd66 <span class="sc">%&gt;%</span></span>
<span id="cb21-16"><a href="#cb21-16" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb21-17"><a href="#cb21-17" tabindex="-1"></a>    <span class="at">age_band =</span> <span class="fu">case_when</span>(</span>
<span id="cb21-18"><a href="#cb21-18" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">18</span> <span class="sc">&amp;</span> age <span class="sc">&lt;=</span> <span class="dv">34</span> <span class="sc">~</span> <span class="st">&quot;18–34&quot;</span>,</span>
<span id="cb21-19"><a href="#cb21-19" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">35</span> <span class="sc">&amp;</span> age <span class="sc">&lt;=</span> <span class="dv">54</span> <span class="sc">~</span> <span class="st">&quot;35–54&quot;</span>,</span>
<span id="cb21-20"><a href="#cb21-20" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">55</span>             <span class="sc">~</span> <span class="st">&quot;55+&quot;</span>,</span>
<span id="cb21-21"><a href="#cb21-21" tabindex="-1"></a>      <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb21-22"><a href="#cb21-22" tabindex="-1"></a>    ),</span>
<span id="cb21-23"><a href="#cb21-23" tabindex="-1"></a>    </span>
<span id="cb21-24"><a href="#cb21-24" tabindex="-1"></a>    <span class="at">gender_lab =</span> <span class="fu">case_when</span>(</span>
<span id="cb21-25"><a href="#cb21-25" tabindex="-1"></a>      female <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>) <span class="sc">~</span> <span class="fu">if_else</span>(female <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb21-26"><a href="#cb21-26" tabindex="-1"></a>      <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb21-27"><a href="#cb21-27" tabindex="-1"></a>    ),</span>
<span id="cb21-28"><a href="#cb21-28" tabindex="-1"></a>    </span>
<span id="cb21-29"><a href="#cb21-29" tabindex="-1"></a>  </span>
<span id="cb21-30"><a href="#cb21-30" tabindex="-1"></a>    <span class="at">edu_lab =</span> <span class="fu">case_when</span>(</span>
<span id="cb21-31"><a href="#cb21-31" tabindex="-1"></a>      education <span class="sc">==</span> <span class="dv">4</span>              <span class="sc">~</span> <span class="st">&quot;With a university degree&quot;</span>,</span>
<span id="cb21-32"><a href="#cb21-32" tabindex="-1"></a>      education <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>)   <span class="sc">~</span> <span class="st">&quot;Without a university degree&quot;</span>,</span>
<span id="cb21-33"><a href="#cb21-33" tabindex="-1"></a>      <span class="cn">TRUE</span>                        <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb21-34"><a href="#cb21-34" tabindex="-1"></a>    )</span>
<span id="cb21-35"><a href="#cb21-35" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
<span id="cb21-36"><a href="#cb21-36" tabindex="-1"></a>  <span class="fu">filter</span>(</span>
<span id="cb21-37"><a href="#cb21-37" tabindex="-1"></a>    <span class="sc">!</span><span class="fu">is.na</span>(age_band),</span>
<span id="cb21-38"><a href="#cb21-38" tabindex="-1"></a>    <span class="sc">!</span><span class="fu">is.na</span>(gender_lab),</span>
<span id="cb21-39"><a href="#cb21-39" tabindex="-1"></a>    <span class="sc">!</span><span class="fu">is.na</span>(edu_lab)</span>
<span id="cb21-40"><a href="#cb21-40" tabindex="-1"></a>  )</span>
<span id="cb21-41"><a href="#cb21-41" tabindex="-1"></a></span>
<span id="cb21-42"><a href="#cb21-42" tabindex="-1"></a></span>
<span id="cb21-43"><a href="#cb21-43" tabindex="-1"></a></span>
<span id="cb21-44"><a href="#cb21-44" tabindex="-1"></a>sample_gender <span class="ot">&lt;-</span> df_demo <span class="sc">%&gt;%</span></span>
<span id="cb21-45"><a href="#cb21-45" tabindex="-1"></a>  <span class="fu">count</span>(gender_lab) <span class="sc">%&gt;%</span></span>
<span id="cb21-46"><a href="#cb21-46" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample =</span> n <span class="sc">/</span> <span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span></span>
<span id="cb21-47"><a href="#cb21-47" tabindex="-1"></a>  <span class="fu">transmute</span>(</span>
<span id="cb21-48"><a href="#cb21-48" tabindex="-1"></a>    <span class="at">indicator =</span> <span class="st">&quot;gender&quot;</span>,</span>
<span id="cb21-49"><a href="#cb21-49" tabindex="-1"></a>    <span class="at">label     =</span> gender_lab,</span>
<span id="cb21-50"><a href="#cb21-50" tabindex="-1"></a>    sample</span>
<span id="cb21-51"><a href="#cb21-51" tabindex="-1"></a>  )</span>
<span id="cb21-52"><a href="#cb21-52" tabindex="-1"></a></span>
<span id="cb21-53"><a href="#cb21-53" tabindex="-1"></a>sample_age <span class="ot">&lt;-</span> df_demo <span class="sc">%&gt;%</span></span>
<span id="cb21-54"><a href="#cb21-54" tabindex="-1"></a>  <span class="fu">count</span>(age_band) <span class="sc">%&gt;%</span></span>
<span id="cb21-55"><a href="#cb21-55" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample =</span> n <span class="sc">/</span> <span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span></span>
<span id="cb21-56"><a href="#cb21-56" tabindex="-1"></a>  <span class="fu">transmute</span>(</span>
<span id="cb21-57"><a href="#cb21-57" tabindex="-1"></a>    <span class="at">indicator =</span> <span class="st">&quot;age&quot;</span>,</span>
<span id="cb21-58"><a href="#cb21-58" tabindex="-1"></a>    <span class="at">label     =</span> age_band,</span>
<span id="cb21-59"><a href="#cb21-59" tabindex="-1"></a>    sample</span>
<span id="cb21-60"><a href="#cb21-60" tabindex="-1"></a>  )</span>
<span id="cb21-61"><a href="#cb21-61" tabindex="-1"></a></span>
<span id="cb21-62"><a href="#cb21-62" tabindex="-1"></a>sample_edu <span class="ot">&lt;-</span> df_demo <span class="sc">%&gt;%</span></span>
<span id="cb21-63"><a href="#cb21-63" tabindex="-1"></a>  <span class="fu">count</span>(edu_lab) <span class="sc">%&gt;%</span></span>
<span id="cb21-64"><a href="#cb21-64" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">sample =</span> n <span class="sc">/</span> <span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span></span>
<span id="cb21-65"><a href="#cb21-65" tabindex="-1"></a>  <span class="fu">transmute</span>(</span>
<span id="cb21-66"><a href="#cb21-66" tabindex="-1"></a>    <span class="at">indicator =</span> <span class="st">&quot;education&quot;</span>,</span>
<span id="cb21-67"><a href="#cb21-67" tabindex="-1"></a>    <span class="at">label     =</span> edu_lab,</span>
<span id="cb21-68"><a href="#cb21-68" tabindex="-1"></a>    sample</span>
<span id="cb21-69"><a href="#cb21-69" tabindex="-1"></a>  )</span>
<span id="cb21-70"><a href="#cb21-70" tabindex="-1"></a></span>
<span id="cb21-71"><a href="#cb21-71" tabindex="-1"></a>sample_all <span class="ot">&lt;-</span> <span class="fu">bind_rows</span>(sample_gender, sample_age, sample_edu)</span>
<span id="cb21-72"><a href="#cb21-72" tabindex="-1"></a></span>
<span id="cb21-73"><a href="#cb21-73" tabindex="-1"></a></span>
<span id="cb21-74"><a href="#cb21-74" tabindex="-1"></a>comp <span class="ot">&lt;-</span> targets_judo <span class="sc">%&gt;%</span></span>
<span id="cb21-75"><a href="#cb21-75" tabindex="-1"></a>  <span class="fu">left_join</span>(sample_all, <span class="at">by =</span> <span class="fu">c</span>(<span class="st">&quot;indicator&quot;</span>, <span class="st">&quot;label&quot;</span>)) <span class="sc">%&gt;%</span></span>
<span id="cb21-76"><a href="#cb21-76" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb21-77"><a href="#cb21-77" tabindex="-1"></a>    <span class="at">Target =</span> target,</span>
<span id="cb21-78"><a href="#cb21-78" tabindex="-1"></a>    <span class="at">Sample =</span> sample,</span>
<span id="cb21-79"><a href="#cb21-79" tabindex="-1"></a>    <span class="at">label_plot =</span> label</span>
<span id="cb21-80"><a href="#cb21-80" tabindex="-1"></a>  )</span>
<span id="cb21-81"><a href="#cb21-81" tabindex="-1"></a></span>
<span id="cb21-82"><a href="#cb21-82" tabindex="-1"></a>p_demo <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(comp, <span class="fu">aes</span>(<span class="at">x =</span> Target, <span class="at">y =</span> Sample)) <span class="sc">+</span></span>
<span id="cb21-83"><a href="#cb21-83" tabindex="-1"></a>  <span class="fu">geom_abline</span>(<span class="at">slope =</span> <span class="dv">1</span>, <span class="at">intercept =</span> <span class="dv">0</span>,</span>
<span id="cb21-84"><a href="#cb21-84" tabindex="-1"></a>              <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="at">color =</span> <span class="st">&quot;grey60&quot;</span>) <span class="sc">+</span></span>
<span id="cb21-85"><a href="#cb21-85" tabindex="-1"></a>  <span class="fu">geom_segment</span>(</span>
<span id="cb21-86"><a href="#cb21-86" tabindex="-1"></a>    <span class="fu">aes</span>(<span class="at">x =</span> Target, <span class="at">xend =</span> Target,</span>
<span id="cb21-87"><a href="#cb21-87" tabindex="-1"></a>        <span class="at">y =</span> Target, <span class="at">yend =</span> Sample,</span>
<span id="cb21-88"><a href="#cb21-88" tabindex="-1"></a>        <span class="at">group =</span> <span class="fu">interaction</span>(indicator, label_plot)),</span>
<span id="cb21-89"><a href="#cb21-89" tabindex="-1"></a>    <span class="at">linewidth =</span> <span class="fl">0.5</span>, <span class="at">alpha =</span> <span class="fl">0.6</span>,</span>
<span id="cb21-90"><a href="#cb21-90" tabindex="-1"></a>    <span class="at">color =</span> <span class="st">&quot;grey50&quot;</span>, <span class="at">linetype =</span> <span class="st">&quot;dotted&quot;</span></span>
<span id="cb21-91"><a href="#cb21-91" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb21-92"><a href="#cb21-92" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="fu">aes</span>(<span class="at">color =</span> indicator), <span class="at">size =</span> <span class="fl">3.5</span>) <span class="sc">+</span></span>
<span id="cb21-93"><a href="#cb21-93" tabindex="-1"></a>  ggrepel<span class="sc">::</span><span class="fu">geom_text_repel</span>(</span>
<span id="cb21-94"><a href="#cb21-94" tabindex="-1"></a>    <span class="fu">aes</span>(<span class="at">label =</span> label_plot, <span class="at">color =</span> indicator),</span>
<span id="cb21-95"><a href="#cb21-95" tabindex="-1"></a>    <span class="at">size =</span> <span class="dv">4</span>, <span class="at">seed =</span> <span class="dv">2024</span>, <span class="at">min.segment.length =</span> <span class="fl">0.1</span>,</span>
<span id="cb21-96"><a href="#cb21-96" tabindex="-1"></a>    <span class="at">box.padding =</span> <span class="fl">0.25</span>, <span class="at">point.padding =</span> <span class="fl">0.25</span>,</span>
<span id="cb21-97"><a href="#cb21-97" tabindex="-1"></a>    <span class="at">show.legend =</span> <span class="cn">FALSE</span></span>
<span id="cb21-98"><a href="#cb21-98" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb21-99"><a href="#cb21-99" tabindex="-1"></a>  <span class="fu">scale_x_continuous</span>(</span>
<span id="cb21-100"><a href="#cb21-100" tabindex="-1"></a>    <span class="at">labels =</span> scales<span class="sc">::</span><span class="fu">percent_format</span>(<span class="at">accuracy =</span> <span class="dv">1</span>),</span>
<span id="cb21-101"><a href="#cb21-101" tabindex="-1"></a>    <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="fu">max</span>(comp<span class="sc">$</span>Target, comp<span class="sc">$</span>Sample, <span class="at">na.rm =</span> <span class="cn">TRUE</span>) <span class="sc">*</span> <span class="fl">1.05</span>)</span>
<span id="cb21-102"><a href="#cb21-102" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb21-103"><a href="#cb21-103" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(</span>
<span id="cb21-104"><a href="#cb21-104" tabindex="-1"></a>    <span class="at">labels =</span> scales<span class="sc">::</span><span class="fu">percent_format</span>(<span class="at">accuracy =</span> <span class="dv">1</span>),</span>
<span id="cb21-105"><a href="#cb21-105" tabindex="-1"></a>    <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="fu">max</span>(comp<span class="sc">$</span>Target, comp<span class="sc">$</span>Sample, <span class="at">na.rm =</span> <span class="cn">TRUE</span>) <span class="sc">*</span> <span class="fl">1.05</span>)</span>
<span id="cb21-106"><a href="#cb21-106" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb21-107"><a href="#cb21-107" tabindex="-1"></a>  <span class="fu">scale_color_manual</span>(</span>
<span id="cb21-108"><a href="#cb21-108" tabindex="-1"></a>    <span class="at">values =</span> <span class="fu">c</span>(</span>
<span id="cb21-109"><a href="#cb21-109" tabindex="-1"></a>      <span class="at">gender    =</span> <span class="st">&quot;steelblue&quot;</span>,</span>
<span id="cb21-110"><a href="#cb21-110" tabindex="-1"></a>      <span class="at">age       =</span> <span class="st">&quot;seagreen&quot;</span>,</span>
<span id="cb21-111"><a href="#cb21-111" tabindex="-1"></a>      <span class="at">education =</span> <span class="st">&quot;goldenrod&quot;</span></span>
<span id="cb21-112"><a href="#cb21-112" tabindex="-1"></a>    )</span>
<span id="cb21-113"><a href="#cb21-113" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb21-114"><a href="#cb21-114" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb21-115"><a href="#cb21-115" tabindex="-1"></a>    <span class="at">x =</span> <span class="st">&quot;Target (national)&quot;</span>,</span>
<span id="cb21-116"><a href="#cb21-116" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Sample (2nd gold medal RDD sample)&quot;</span></span>
<span id="cb21-117"><a href="#cb21-117" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb21-118"><a href="#cb21-118" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb21-119"><a href="#cb21-119" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb21-120"><a href="#cb21-120" tabindex="-1"></a>    <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb21-121"><a href="#cb21-121" tabindex="-1"></a>    <span class="at">legend.position  =</span> <span class="st">&quot;none&quot;</span></span>
<span id="cb21-122"><a href="#cb21-122" tabindex="-1"></a>  )</span>
<span id="cb21-123"><a href="#cb21-123" tabindex="-1"></a></span>
<span id="cb21-124"><a href="#cb21-124" tabindex="-1"></a><span class="fu">print</span>(p_demo)</span></code></pre></div>
<p><img role="img" 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" width="672" /></p>
</div>
<div id="figure-d3-sample-characteristics-tend-to-diverge-substantially-between-the-pre--and-post--treatment-periods-1st-gold-medal" class="section level3 unnumbered">
<h3 class="unnumbered">Figure D3: Sample characteristics tend to diverge
substantially between the pre- and post- treatment periods (1st gold
medal)</h3>
<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1" tabindex="-1"></a>demo_time <span class="ot">&lt;-</span> judo48p <span class="sc">%&gt;%</span></span>
<span id="cb22-2"><a href="#cb22-2" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb22-3"><a href="#cb22-3" tabindex="-1"></a>    <span class="at">time_bin =</span> <span class="fu">floor_date</span>(StartDate, <span class="at">unit =</span> <span class="st">&quot;1 hour&quot;</span>),</span>
<span id="cb22-4"><a href="#cb22-4" tabindex="-1"></a>    </span>
<span id="cb22-5"><a href="#cb22-5" tabindex="-1"></a>    <span class="at">gender_lab =</span> <span class="fu">case_when</span>(</span>
<span id="cb22-6"><a href="#cb22-6" tabindex="-1"></a>      female <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>) <span class="sc">~</span> <span class="fu">if_else</span>(female <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb22-7"><a href="#cb22-7" tabindex="-1"></a>      <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb22-8"><a href="#cb22-8" tabindex="-1"></a>    ),</span>
<span id="cb22-9"><a href="#cb22-9" tabindex="-1"></a>    </span>
<span id="cb22-10"><a href="#cb22-10" tabindex="-1"></a>    <span class="at">age_band =</span> <span class="fu">case_when</span>(</span>
<span id="cb22-11"><a href="#cb22-11" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">18</span> <span class="sc">&amp;</span> age <span class="sc">&lt;=</span> <span class="dv">34</span> <span class="sc">~</span> <span class="st">&quot;18–34&quot;</span>,</span>
<span id="cb22-12"><a href="#cb22-12" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">35</span> <span class="sc">&amp;</span> age <span class="sc">&lt;=</span> <span class="dv">54</span> <span class="sc">~</span> <span class="st">&quot;35–54&quot;</span>,</span>
<span id="cb22-13"><a href="#cb22-13" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">55</span>             <span class="sc">~</span> <span class="st">&quot;55+&quot;</span>,</span>
<span id="cb22-14"><a href="#cb22-14" tabindex="-1"></a>      <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb22-15"><a href="#cb22-15" tabindex="-1"></a>    ),</span>
<span id="cb22-16"><a href="#cb22-16" tabindex="-1"></a>    </span>
<span id="cb22-17"><a href="#cb22-17" tabindex="-1"></a>    <span class="at">edu_lab =</span> <span class="fu">case_when</span>(</span>
<span id="cb22-18"><a href="#cb22-18" tabindex="-1"></a>      education <span class="sc">==</span> <span class="dv">4</span>              <span class="sc">~</span> <span class="st">&quot;With a university degree&quot;</span>,</span>
<span id="cb22-19"><a href="#cb22-19" tabindex="-1"></a>      education <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>)   <span class="sc">~</span> <span class="st">&quot;Without a university degree&quot;</span>,</span>
<span id="cb22-20"><a href="#cb22-20" tabindex="-1"></a>      <span class="cn">TRUE</span>                        <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb22-21"><a href="#cb22-21" tabindex="-1"></a>    ),</span>
<span id="cb22-22"><a href="#cb22-22" tabindex="-1"></a>    </span>
<span id="cb22-23"><a href="#cb22-23" tabindex="-1"></a>    <span class="at">income_band =</span> <span class="fu">case_when</span>(</span>
<span id="cb22-24"><a href="#cb22-24" tabindex="-1"></a>      income <span class="sc">%in%</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>    <span class="sc">~</span> <span class="st">&quot;Low&quot;</span>,</span>
<span id="cb22-25"><a href="#cb22-25" tabindex="-1"></a>      income <span class="sc">%in%</span> <span class="dv">6</span><span class="sc">:</span><span class="dv">10</span>   <span class="sc">~</span> <span class="st">&quot;Middle&quot;</span>,</span>
<span id="cb22-26"><a href="#cb22-26" tabindex="-1"></a>      income <span class="sc">%in%</span> <span class="dv">11</span><span class="sc">:</span><span class="dv">14</span>  <span class="sc">~</span> <span class="st">&quot;High&quot;</span>,</span>
<span id="cb22-27"><a href="#cb22-27" tabindex="-1"></a>      <span class="cn">TRUE</span>               <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb22-28"><a href="#cb22-28" tabindex="-1"></a>    )</span>
<span id="cb22-29"><a href="#cb22-29" tabindex="-1"></a>  )</span>
<span id="cb22-30"><a href="#cb22-30" tabindex="-1"></a></span>
<span id="cb22-31"><a href="#cb22-31" tabindex="-1"></a></span>
<span id="cb22-32"><a href="#cb22-32" tabindex="-1"></a>demo_long <span class="ot">&lt;-</span> demo_time <span class="sc">%&gt;%</span></span>
<span id="cb22-33"><a href="#cb22-33" tabindex="-1"></a>  <span class="fu">select</span>(time_bin, gender_lab, age_band, edu_lab, income_band) <span class="sc">%&gt;%</span></span>
<span id="cb22-34"><a href="#cb22-34" tabindex="-1"></a>  </span>
<span id="cb22-35"><a href="#cb22-35" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(</span>
<span id="cb22-36"><a href="#cb22-36" tabindex="-1"></a>    <span class="at">cols      =</span> <span class="fu">c</span>(gender_lab, age_band, edu_lab, income_band),</span>
<span id="cb22-37"><a href="#cb22-37" tabindex="-1"></a>    <span class="at">names_to  =</span> <span class="st">&quot;indicator&quot;</span>,</span>
<span id="cb22-38"><a href="#cb22-38" tabindex="-1"></a>    <span class="at">values_to =</span> <span class="st">&quot;label&quot;</span></span>
<span id="cb22-39"><a href="#cb22-39" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
<span id="cb22-40"><a href="#cb22-40" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(label)) <span class="sc">%&gt;%</span></span>
<span id="cb22-41"><a href="#cb22-41" tabindex="-1"></a>  </span>
<span id="cb22-42"><a href="#cb22-42" tabindex="-1"></a>  <span class="fu">group_by</span>(indicator, time_bin, label) <span class="sc">%&gt;%</span></span>
<span id="cb22-43"><a href="#cb22-43" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n =</span> <span class="fu">n</span>(), <span class="at">.groups =</span> <span class="st">&quot;drop_last&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb22-44"><a href="#cb22-44" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">prop =</span> n <span class="sc">/</span> <span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span></span>
<span id="cb22-45"><a href="#cb22-45" tabindex="-1"></a>  <span class="fu">ungroup</span>() <span class="sc">%&gt;%</span></span>
<span id="cb22-46"><a href="#cb22-46" tabindex="-1"></a>  </span>
<span id="cb22-47"><a href="#cb22-47" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb22-48"><a href="#cb22-48" tabindex="-1"></a>    <span class="at">indicator =</span> dplyr<span class="sc">::</span><span class="fu">recode</span>(</span>
<span id="cb22-49"><a href="#cb22-49" tabindex="-1"></a>      indicator,</span>
<span id="cb22-50"><a href="#cb22-50" tabindex="-1"></a>      <span class="at">gender_lab  =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb22-51"><a href="#cb22-51" tabindex="-1"></a>      <span class="at">age_band    =</span> <span class="st">&quot;Age&quot;</span>,</span>
<span id="cb22-52"><a href="#cb22-52" tabindex="-1"></a>      <span class="at">edu_lab     =</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb22-53"><a href="#cb22-53" tabindex="-1"></a>      <span class="at">income_band =</span> <span class="st">&quot;Income&quot;</span></span>
<span id="cb22-54"><a href="#cb22-54" tabindex="-1"></a>    )</span>
<span id="cb22-55"><a href="#cb22-55" tabindex="-1"></a>  )</span>
<span id="cb22-56"><a href="#cb22-56" tabindex="-1"></a></span>
<span id="cb22-57"><a href="#cb22-57" tabindex="-1"></a>end_labels <span class="ot">&lt;-</span> demo_long <span class="sc">%&gt;%</span></span>
<span id="cb22-58"><a href="#cb22-58" tabindex="-1"></a>  <span class="fu">group_by</span>(indicator, label) <span class="sc">%&gt;%</span></span>
<span id="cb22-59"><a href="#cb22-59" tabindex="-1"></a>  <span class="fu">filter</span>(time_bin <span class="sc">==</span> <span class="fu">max</span>(time_bin)) <span class="sc">%&gt;%</span></span>
<span id="cb22-60"><a href="#cb22-60" tabindex="-1"></a>  <span class="fu">ungroup</span>()</span>
<span id="cb22-61"><a href="#cb22-61" tabindex="-1"></a></span>
<span id="cb22-62"><a href="#cb22-62" tabindex="-1"></a>vline_time <span class="ot">&lt;-</span> <span class="fu">as.POSIXct</span>(<span class="st">&quot;2024/07/28 01:01&quot;</span>,</span>
<span id="cb22-63"><a href="#cb22-63" tabindex="-1"></a>                         <span class="at">format =</span> <span class="st">&quot;%Y/%m/%d %H:%M&quot;</span>,</span>
<span id="cb22-64"><a href="#cb22-64" tabindex="-1"></a>                         <span class="at">tz =</span> <span class="st">&quot;Asia/Tokyo&quot;</span>)</span>
<span id="cb22-65"><a href="#cb22-65" tabindex="-1"></a></span>
<span id="cb22-66"><a href="#cb22-66" tabindex="-1"></a>p_demo_time <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(demo_long,</span>
<span id="cb22-67"><a href="#cb22-67" tabindex="-1"></a>                      <span class="fu">aes</span>(<span class="at">x =</span> time_bin, <span class="at">y =</span> prop, <span class="at">color =</span> label, <span class="at">group =</span> label)) <span class="sc">+</span></span>
<span id="cb22-68"><a href="#cb22-68" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb22-69"><a href="#cb22-69" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb22-70"><a href="#cb22-70" tabindex="-1"></a>  <span class="fu">geom_text</span>(</span>
<span id="cb22-71"><a href="#cb22-71" tabindex="-1"></a>    <span class="fu">aes</span>(<span class="at">label =</span> scales<span class="sc">::</span><span class="fu">percent</span>(prop, <span class="at">accuracy =</span> <span class="dv">1</span>)),</span>
<span id="cb22-72"><a href="#cb22-72" tabindex="-1"></a>    <span class="at">vjust =</span> <span class="sc">-</span><span class="fl">0.4</span>, <span class="at">size =</span> <span class="fl">2.5</span>, <span class="at">show.legend =</span> <span class="cn">FALSE</span></span>
<span id="cb22-73"><a href="#cb22-73" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb22-74"><a href="#cb22-74" tabindex="-1"></a>  <span class="fu">geom_vline</span>(</span>
<span id="cb22-75"><a href="#cb22-75" tabindex="-1"></a>    <span class="at">xintercept =</span> vline_time,</span>
<span id="cb22-76"><a href="#cb22-76" tabindex="-1"></a>    <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>,</span>
<span id="cb22-77"><a href="#cb22-77" tabindex="-1"></a>    <span class="at">color =</span> <span class="st">&quot;red&quot;</span></span>
<span id="cb22-78"><a href="#cb22-78" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb22-79"><a href="#cb22-79" tabindex="-1"></a>  ggrepel<span class="sc">::</span><span class="fu">geom_text_repel</span>(</span>
<span id="cb22-80"><a href="#cb22-80" tabindex="-1"></a>    <span class="at">data =</span> end_labels,</span>
<span id="cb22-81"><a href="#cb22-81" tabindex="-1"></a>    <span class="fu">aes</span>(<span class="at">label =</span> label),</span>
<span id="cb22-82"><a href="#cb22-82" tabindex="-1"></a>    <span class="at">size =</span> <span class="dv">3</span>,</span>
<span id="cb22-83"><a href="#cb22-83" tabindex="-1"></a>    <span class="at">show.legend =</span> <span class="cn">FALSE</span>,</span>
<span id="cb22-84"><a href="#cb22-84" tabindex="-1"></a>    <span class="at">segment.colour =</span> <span class="cn">NA</span>,  </span>
<span id="cb22-85"><a href="#cb22-85" tabindex="-1"></a>    <span class="at">nudge_x =</span> <span class="dv">0</span>,          </span>
<span id="cb22-86"><a href="#cb22-86" tabindex="-1"></a>    <span class="at">direction =</span> <span class="st">&quot;y&quot;</span>,     </span>
<span id="cb22-87"><a href="#cb22-87" tabindex="-1"></a>    <span class="at">min.segment.length =</span> <span class="dv">0</span></span>
<span id="cb22-88"><a href="#cb22-88" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb22-89"><a href="#cb22-89" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> indicator, <span class="at">ncol =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb22-90"><a href="#cb22-90" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(</span>
<span id="cb22-91"><a href="#cb22-91" tabindex="-1"></a>    <span class="at">labels =</span> scales<span class="sc">::</span><span class="fu">percent_format</span>(<span class="at">accuracy =</span> <span class="dv">1</span>),</span>
<span id="cb22-92"><a href="#cb22-92" tabindex="-1"></a>    <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)</span>
<span id="cb22-93"><a href="#cb22-93" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb22-94"><a href="#cb22-94" tabindex="-1"></a>  <span class="fu">scale_x_datetime</span>(</span>
<span id="cb22-95"><a href="#cb22-95" tabindex="-1"></a>    <span class="at">date_breaks =</span> <span class="st">&quot;1 hour&quot;</span>,</span>
<span id="cb22-96"><a href="#cb22-96" tabindex="-1"></a>    <span class="at">date_labels =</span> <span class="st">&quot;%H:%M&quot;</span></span>
<span id="cb22-97"><a href="#cb22-97" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb22-98"><a href="#cb22-98" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb22-99"><a href="#cb22-99" tabindex="-1"></a>    <span class="at">x =</span> <span class="st">&quot;Survey time (JST)&quot;</span>,</span>
<span id="cb22-100"><a href="#cb22-100" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Share within each category&quot;</span>,</span>
<span id="cb22-101"><a href="#cb22-101" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb22-102"><a href="#cb22-102" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb22-103"><a href="#cb22-103" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb22-104"><a href="#cb22-104" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb22-105"><a href="#cb22-105" tabindex="-1"></a>    <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>,        </span>
<span id="cb22-106"><a href="#cb22-106" tabindex="-1"></a>    <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb22-107"><a href="#cb22-107" tabindex="-1"></a>    <span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">hjust =</span> <span class="dv">1</span>) </span>
<span id="cb22-108"><a href="#cb22-108" tabindex="-1"></a>  )</span>
<span id="cb22-109"><a href="#cb22-109" tabindex="-1"></a></span>
<span id="cb22-110"><a href="#cb22-110" tabindex="-1"></a><span class="fu">print</span>(p_demo_time)</span></code></pre></div>
<p><img role="img" 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" width="672" /></p>
</div>
<div id="figure-d4-sample-characteristics-tend-to-diverge-substantially-between-the-pre--and-post--treatment-periods-2nd-gold-medal" class="section level3 unnumbered">
<h3 class="unnumbered">Figure D4: Sample characteristics tend to diverge
substantially between the pre- and post- treatment periods (2nd gold
medal)</h3>
<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1" tabindex="-1"></a>demo_time <span class="ot">&lt;-</span> judo66p <span class="sc">%&gt;%</span></span>
<span id="cb23-2"><a href="#cb23-2" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb23-3"><a href="#cb23-3" tabindex="-1"></a>    <span class="at">time_bin =</span> <span class="fu">floor_date</span>(StartDate, <span class="at">unit =</span> <span class="st">&quot;1 hour&quot;</span>),</span>
<span id="cb23-4"><a href="#cb23-4" tabindex="-1"></a>    </span>
<span id="cb23-5"><a href="#cb23-5" tabindex="-1"></a>    <span class="at">gender_lab =</span> <span class="fu">case_when</span>(</span>
<span id="cb23-6"><a href="#cb23-6" tabindex="-1"></a>      female <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>) <span class="sc">~</span> <span class="fu">if_else</span>(female <span class="sc">==</span> <span class="dv">1</span>, <span class="st">&quot;Female&quot;</span>, <span class="st">&quot;Male&quot;</span>),</span>
<span id="cb23-7"><a href="#cb23-7" tabindex="-1"></a>      <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb23-8"><a href="#cb23-8" tabindex="-1"></a>    ),</span>
<span id="cb23-9"><a href="#cb23-9" tabindex="-1"></a>    </span>
<span id="cb23-10"><a href="#cb23-10" tabindex="-1"></a>    <span class="at">age_band =</span> <span class="fu">case_when</span>(</span>
<span id="cb23-11"><a href="#cb23-11" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">18</span> <span class="sc">&amp;</span> age <span class="sc">&lt;=</span> <span class="dv">34</span> <span class="sc">~</span> <span class="st">&quot;18–34&quot;</span>,</span>
<span id="cb23-12"><a href="#cb23-12" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">35</span> <span class="sc">&amp;</span> age <span class="sc">&lt;=</span> <span class="dv">54</span> <span class="sc">~</span> <span class="st">&quot;35–54&quot;</span>,</span>
<span id="cb23-13"><a href="#cb23-13" tabindex="-1"></a>      age <span class="sc">&gt;=</span> <span class="dv">55</span>             <span class="sc">~</span> <span class="st">&quot;55+&quot;</span>,</span>
<span id="cb23-14"><a href="#cb23-14" tabindex="-1"></a>      <span class="cn">TRUE</span> <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb23-15"><a href="#cb23-15" tabindex="-1"></a>    ),</span>
<span id="cb23-16"><a href="#cb23-16" tabindex="-1"></a>    </span>
<span id="cb23-17"><a href="#cb23-17" tabindex="-1"></a>    <span class="at">edu_lab =</span> <span class="fu">case_when</span>(</span>
<span id="cb23-18"><a href="#cb23-18" tabindex="-1"></a>      education <span class="sc">==</span> <span class="dv">4</span>              <span class="sc">~</span> <span class="st">&quot;With a university degree&quot;</span>,</span>
<span id="cb23-19"><a href="#cb23-19" tabindex="-1"></a>      education <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>, <span class="dv">3</span>)   <span class="sc">~</span> <span class="st">&quot;Without a university degree&quot;</span>,</span>
<span id="cb23-20"><a href="#cb23-20" tabindex="-1"></a>      <span class="cn">TRUE</span>                        <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb23-21"><a href="#cb23-21" tabindex="-1"></a>    ),</span>
<span id="cb23-22"><a href="#cb23-22" tabindex="-1"></a>    </span>
<span id="cb23-23"><a href="#cb23-23" tabindex="-1"></a>    <span class="at">income_band =</span> <span class="fu">case_when</span>(</span>
<span id="cb23-24"><a href="#cb23-24" tabindex="-1"></a>      income <span class="sc">%in%</span> <span class="dv">1</span><span class="sc">:</span><span class="dv">5</span>    <span class="sc">~</span> <span class="st">&quot;Low&quot;</span>,</span>
<span id="cb23-25"><a href="#cb23-25" tabindex="-1"></a>      income <span class="sc">%in%</span> <span class="dv">6</span><span class="sc">:</span><span class="dv">10</span>   <span class="sc">~</span> <span class="st">&quot;Middle&quot;</span>,</span>
<span id="cb23-26"><a href="#cb23-26" tabindex="-1"></a>      income <span class="sc">%in%</span> <span class="dv">11</span><span class="sc">:</span><span class="dv">14</span>  <span class="sc">~</span> <span class="st">&quot;High&quot;</span>,</span>
<span id="cb23-27"><a href="#cb23-27" tabindex="-1"></a>      <span class="cn">TRUE</span>               <span class="sc">~</span> <span class="cn">NA_character_</span></span>
<span id="cb23-28"><a href="#cb23-28" tabindex="-1"></a>    )</span>
<span id="cb23-29"><a href="#cb23-29" tabindex="-1"></a>  )</span>
<span id="cb23-30"><a href="#cb23-30" tabindex="-1"></a></span>
<span id="cb23-31"><a href="#cb23-31" tabindex="-1"></a>demo_long <span class="ot">&lt;-</span> demo_time <span class="sc">%&gt;%</span></span>
<span id="cb23-32"><a href="#cb23-32" tabindex="-1"></a>  <span class="fu">select</span>(time_bin, gender_lab, age_band, edu_lab, income_band) <span class="sc">%&gt;%</span></span>
<span id="cb23-33"><a href="#cb23-33" tabindex="-1"></a>  </span>
<span id="cb23-34"><a href="#cb23-34" tabindex="-1"></a>  <span class="fu">pivot_longer</span>(</span>
<span id="cb23-35"><a href="#cb23-35" tabindex="-1"></a>    <span class="at">cols      =</span> <span class="fu">c</span>(gender_lab, age_band, edu_lab, income_band),</span>
<span id="cb23-36"><a href="#cb23-36" tabindex="-1"></a>    <span class="at">names_to  =</span> <span class="st">&quot;indicator&quot;</span>,</span>
<span id="cb23-37"><a href="#cb23-37" tabindex="-1"></a>    <span class="at">values_to =</span> <span class="st">&quot;label&quot;</span></span>
<span id="cb23-38"><a href="#cb23-38" tabindex="-1"></a>  ) <span class="sc">%&gt;%</span></span>
<span id="cb23-39"><a href="#cb23-39" tabindex="-1"></a>  <span class="fu">filter</span>(<span class="sc">!</span><span class="fu">is.na</span>(label)) <span class="sc">%&gt;%</span></span>
<span id="cb23-40"><a href="#cb23-40" tabindex="-1"></a>  </span>
<span id="cb23-41"><a href="#cb23-41" tabindex="-1"></a>  <span class="fu">group_by</span>(indicator, time_bin, label) <span class="sc">%&gt;%</span></span>
<span id="cb23-42"><a href="#cb23-42" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n =</span> <span class="fu">n</span>(), <span class="at">.groups =</span> <span class="st">&quot;drop_last&quot;</span>) <span class="sc">%&gt;%</span></span>
<span id="cb23-43"><a href="#cb23-43" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">prop =</span> n <span class="sc">/</span> <span class="fu">sum</span>(n)) <span class="sc">%&gt;%</span></span>
<span id="cb23-44"><a href="#cb23-44" tabindex="-1"></a>  <span class="fu">ungroup</span>() <span class="sc">%&gt;%</span></span>
<span id="cb23-45"><a href="#cb23-45" tabindex="-1"></a>  </span>
<span id="cb23-46"><a href="#cb23-46" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb23-47"><a href="#cb23-47" tabindex="-1"></a>    <span class="at">indicator =</span> dplyr<span class="sc">::</span><span class="fu">recode</span>(</span>
<span id="cb23-48"><a href="#cb23-48" tabindex="-1"></a>      indicator,</span>
<span id="cb23-49"><a href="#cb23-49" tabindex="-1"></a>      <span class="at">gender_lab  =</span> <span class="st">&quot;Gender&quot;</span>,</span>
<span id="cb23-50"><a href="#cb23-50" tabindex="-1"></a>      <span class="at">age_band    =</span> <span class="st">&quot;Age&quot;</span>,</span>
<span id="cb23-51"><a href="#cb23-51" tabindex="-1"></a>      <span class="at">edu_lab     =</span> <span class="st">&quot;Education&quot;</span>,</span>
<span id="cb23-52"><a href="#cb23-52" tabindex="-1"></a>      <span class="at">income_band =</span> <span class="st">&quot;Income&quot;</span></span>
<span id="cb23-53"><a href="#cb23-53" tabindex="-1"></a>    )</span>
<span id="cb23-54"><a href="#cb23-54" tabindex="-1"></a>  )</span>
<span id="cb23-55"><a href="#cb23-55" tabindex="-1"></a></span>
<span id="cb23-56"><a href="#cb23-56" tabindex="-1"></a>end_labels <span class="ot">&lt;-</span> demo_long <span class="sc">%&gt;%</span></span>
<span id="cb23-57"><a href="#cb23-57" tabindex="-1"></a>  <span class="fu">group_by</span>(indicator, label) <span class="sc">%&gt;%</span></span>
<span id="cb23-58"><a href="#cb23-58" tabindex="-1"></a>  <span class="fu">filter</span>(time_bin <span class="sc">==</span> <span class="fu">max</span>(time_bin)) <span class="sc">%&gt;%</span></span>
<span id="cb23-59"><a href="#cb23-59" tabindex="-1"></a>  <span class="fu">ungroup</span>()</span>
<span id="cb23-60"><a href="#cb23-60" tabindex="-1"></a></span>
<span id="cb23-61"><a href="#cb23-61" tabindex="-1"></a>vline_time <span class="ot">&lt;-</span> <span class="fu">as.POSIXct</span>(<span class="st">&quot;2024-07-29 00:56:18&quot;</span>,</span>
<span id="cb23-62"><a href="#cb23-62" tabindex="-1"></a>                         <span class="at">format =</span> <span class="st">&quot;%Y-%m-%d %H:%M:%S&quot;</span>, <span class="at">tz =</span> <span class="st">&quot;Asia/Tokyo&quot;</span>)</span>
<span id="cb23-63"><a href="#cb23-63" tabindex="-1"></a></span>
<span id="cb23-64"><a href="#cb23-64" tabindex="-1"></a>p_demo_time <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(demo_long,</span>
<span id="cb23-65"><a href="#cb23-65" tabindex="-1"></a>                      <span class="fu">aes</span>(<span class="at">x =</span> time_bin, <span class="at">y =</span> prop, <span class="at">color =</span> label, <span class="at">group =</span> label)) <span class="sc">+</span></span>
<span id="cb23-66"><a href="#cb23-66" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb23-67"><a href="#cb23-67" tabindex="-1"></a>  <span class="fu">geom_point</span>(<span class="at">size =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb23-68"><a href="#cb23-68" tabindex="-1"></a>  <span class="fu">geom_text</span>(</span>
<span id="cb23-69"><a href="#cb23-69" tabindex="-1"></a>    <span class="fu">aes</span>(<span class="at">label =</span> scales<span class="sc">::</span><span class="fu">percent</span>(prop, <span class="at">accuracy =</span> <span class="dv">1</span>)),</span>
<span id="cb23-70"><a href="#cb23-70" tabindex="-1"></a>    <span class="at">vjust =</span> <span class="sc">-</span><span class="fl">0.4</span>, <span class="at">size =</span> <span class="fl">2.5</span>, <span class="at">show.legend =</span> <span class="cn">FALSE</span></span>
<span id="cb23-71"><a href="#cb23-71" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb23-72"><a href="#cb23-72" tabindex="-1"></a>  <span class="fu">geom_vline</span>(</span>
<span id="cb23-73"><a href="#cb23-73" tabindex="-1"></a>    <span class="at">xintercept =</span> vline_time,</span>
<span id="cb23-74"><a href="#cb23-74" tabindex="-1"></a>    <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>,</span>
<span id="cb23-75"><a href="#cb23-75" tabindex="-1"></a>    <span class="at">color =</span> <span class="st">&quot;red&quot;</span></span>
<span id="cb23-76"><a href="#cb23-76" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb23-77"><a href="#cb23-77" tabindex="-1"></a>  ggrepel<span class="sc">::</span><span class="fu">geom_text_repel</span>(</span>
<span id="cb23-78"><a href="#cb23-78" tabindex="-1"></a>    <span class="at">data =</span> end_labels,</span>
<span id="cb23-79"><a href="#cb23-79" tabindex="-1"></a>    <span class="fu">aes</span>(<span class="at">label =</span> label),</span>
<span id="cb23-80"><a href="#cb23-80" tabindex="-1"></a>    <span class="at">size =</span> <span class="dv">3</span>,</span>
<span id="cb23-81"><a href="#cb23-81" tabindex="-1"></a>    <span class="at">show.legend =</span> <span class="cn">FALSE</span>,</span>
<span id="cb23-82"><a href="#cb23-82" tabindex="-1"></a>    <span class="at">segment.colour =</span> <span class="cn">NA</span>,   </span>
<span id="cb23-83"><a href="#cb23-83" tabindex="-1"></a>    <span class="at">nudge_x =</span> <span class="dv">0</span>,          </span>
<span id="cb23-84"><a href="#cb23-84" tabindex="-1"></a>    <span class="at">direction =</span> <span class="st">&quot;y&quot;</span>,       </span>
<span id="cb23-85"><a href="#cb23-85" tabindex="-1"></a>    <span class="at">min.segment.length =</span> <span class="dv">0</span></span>
<span id="cb23-86"><a href="#cb23-86" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb23-87"><a href="#cb23-87" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span> indicator, <span class="at">ncol =</span> <span class="dv">2</span>) <span class="sc">+</span></span>
<span id="cb23-88"><a href="#cb23-88" tabindex="-1"></a>  <span class="fu">scale_y_continuous</span>(</span>
<span id="cb23-89"><a href="#cb23-89" tabindex="-1"></a>    <span class="at">labels =</span> <span class="fu">percent_format</span>(<span class="at">accuracy =</span> <span class="dv">1</span>),</span>
<span id="cb23-90"><a href="#cb23-90" tabindex="-1"></a>    <span class="at">limits =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>)</span>
<span id="cb23-91"><a href="#cb23-91" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb23-92"><a href="#cb23-92" tabindex="-1"></a>  <span class="fu">scale_x_datetime</span>(</span>
<span id="cb23-93"><a href="#cb23-93" tabindex="-1"></a>    <span class="at">date_breaks =</span> <span class="st">&quot;1 hour&quot;</span>,</span>
<span id="cb23-94"><a href="#cb23-94" tabindex="-1"></a>    <span class="at">date_labels =</span> <span class="st">&quot;%H:%M&quot;</span></span>
<span id="cb23-95"><a href="#cb23-95" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb23-96"><a href="#cb23-96" tabindex="-1"></a>  <span class="fu">labs</span>(</span>
<span id="cb23-97"><a href="#cb23-97" tabindex="-1"></a>    <span class="at">x =</span> <span class="st">&quot;Survey time (JST)&quot;</span>,</span>
<span id="cb23-98"><a href="#cb23-98" tabindex="-1"></a>    <span class="at">y =</span> <span class="st">&quot;Share within each category&quot;</span>,</span>
<span id="cb23-99"><a href="#cb23-99" tabindex="-1"></a>    <span class="at">color =</span> <span class="cn">NULL</span></span>
<span id="cb23-100"><a href="#cb23-100" tabindex="-1"></a>  ) <span class="sc">+</span></span>
<span id="cb23-101"><a href="#cb23-101" tabindex="-1"></a>  <span class="fu">theme_bw</span>(<span class="at">base_size =</span> <span class="dv">12</span>) <span class="sc">+</span></span>
<span id="cb23-102"><a href="#cb23-102" tabindex="-1"></a>  <span class="fu">theme</span>(</span>
<span id="cb23-103"><a href="#cb23-103" tabindex="-1"></a>    <span class="at">legend.position =</span> <span class="st">&quot;none&quot;</span>,         </span>
<span id="cb23-104"><a href="#cb23-104" tabindex="-1"></a>    <span class="at">panel.grid.minor =</span> <span class="fu">element_blank</span>(),</span>
<span id="cb23-105"><a href="#cb23-105" tabindex="-1"></a>    <span class="at">axis.text.x =</span> <span class="fu">element_text</span>(<span class="at">angle =</span> <span class="dv">45</span>, <span class="at">hjust =</span> <span class="dv">1</span>) </span>
<span id="cb23-106"><a href="#cb23-106" tabindex="-1"></a>  )</span>
<span id="cb23-107"><a href="#cb23-107" tabindex="-1"></a></span>
<span id="cb23-108"><a href="#cb23-108" tabindex="-1"></a><span class="fu">print</span>(p_demo_time)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
</div>
<div id="table-d1-balance-check-results-represent-a-clear-imbalance-between-the-treatment-and-control-groups-the-first-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D1: Balance check results represent a clear
imbalance between the treatment and control groups (The first gold medal
case)</h3>
<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb24-1"><a href="#cb24-1" tabindex="-1"></a>tb_bc1 <span class="ot">&lt;-</span> df_rdd48 <span class="sc">|&gt;</span></span>
<span id="cb24-2"><a href="#cb24-2" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(cutoff, age, female, education, income, psu_rul) <span class="sc">|&gt;</span></span>
<span id="cb24-3"><a href="#cb24-3" tabindex="-1"></a>  tidyr<span class="sc">::</span><span class="fu">drop_na</span>() <span class="sc">|&gt;</span></span>
<span id="cb24-4"><a href="#cb24-4" tabindex="-1"></a>  vtable<span class="sc">::</span><span class="fu">sumtable</span>(<span class="at">group =</span> <span class="st">&quot;cutoff&quot;</span>, <span class="at">group.test =</span> <span class="cn">TRUE</span>, <span class="at">title =</span> <span class="st">&quot;&quot;</span>)</span></code></pre></div>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
cutoff
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
0
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
1
</div>
</th>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
</tr>
<tr>
<th style="text-align:left;">
Variable
</th>
<th style="text-align:left;">
N
</th>
<th style="text-align:left;">
Mean
</th>
<th style="text-align:left;">
SD
</th>
<th style="text-align:left;">
N
</th>
<th style="text-align:left;">
Mean
</th>
<th style="text-align:left;">
SD
</th>
<th style="text-align:left;">
Test
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
age
</td>
<td style="text-align:left;">
558
</td>
<td style="text-align:left;">
50
</td>
<td style="text-align:left;">
14
</td>
<td style="text-align:left;">
182
</td>
<td style="text-align:left;">
63
</td>
<td style="text-align:left;">
17
</td>
<td style="text-align:left;">
F=118.785<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
female
</td>
<td style="text-align:left;">
558
</td>
<td style="text-align:left;">
0.32
</td>
<td style="text-align:left;">
0.47
</td>
<td style="text-align:left;">
182
</td>
<td style="text-align:left;">
0.69
</td>
<td style="text-align:left;">
0.46
</td>
<td style="text-align:left;">
F=88.224<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
education
</td>
<td style="text-align:left;">
558
</td>
<td style="text-align:left;">
3.3
</td>
<td style="text-align:left;">
0.91
</td>
<td style="text-align:left;">
182
</td>
<td style="text-align:left;">
3
</td>
<td style="text-align:left;">
0.92
</td>
<td style="text-align:left;">
F=15.873<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
income
</td>
<td style="text-align:left;">
558
</td>
<td style="text-align:left;">
7.2
</td>
<td style="text-align:left;">
3.3
</td>
<td style="text-align:left;">
182
</td>
<td style="text-align:left;">
6
</td>
<td style="text-align:left;">
2.8
</td>
<td style="text-align:left;">
F=18.208<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
psu_rul
</td>
<td style="text-align:left;">
558
</td>
<td style="text-align:left;">
0.3
</td>
<td style="text-align:left;">
0.46
</td>
<td style="text-align:left;">
182
</td>
<td style="text-align:left;">
0.32
</td>
<td style="text-align:left;">
0.47
</td>
<td style="text-align:left;">
F=0.2<sup></sup>
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; border:0;" colspan="100%">
<sup></sup> Statistical significance markers: * p&lt;0.1; ** p&lt;0.05;
*** p&lt;0.01
</td>
</tr>
</tfoot>
</table>
</div>
<div id="table-d2-balance-check-results-also-represent-a-clear-imbalance-between-the-treatment-and-control-groups-the-second-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D2: Balance check results also represent a
clear imbalance between the treatment and control groups (The second
gold medal case)</h3>
<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1" tabindex="-1"></a>tb_bc2 <span class="ot">&lt;-</span> df_rdd66 <span class="sc">|&gt;</span></span>
<span id="cb25-2"><a href="#cb25-2" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(cutoff, age, female, education, income, psu_rul) <span class="sc">|&gt;</span></span>
<span id="cb25-3"><a href="#cb25-3" tabindex="-1"></a>  tidyr<span class="sc">::</span><span class="fu">drop_na</span>() <span class="sc">|&gt;</span></span>
<span id="cb25-4"><a href="#cb25-4" tabindex="-1"></a>  vtable<span class="sc">::</span><span class="fu">sumtable</span>(<span class="at">group =</span> <span class="st">&quot;cutoff&quot;</span>, <span class="at">group.test =</span> <span class="cn">TRUE</span>, <span class="at">title =</span> <span class="st">&quot;&quot;</span>)</span></code></pre></div>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
cutoff
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
0
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
1
</div>
</th>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
</tr>
<tr>
<th style="text-align:left;">
Variable
</th>
<th style="text-align:left;">
N
</th>
<th style="text-align:left;">
Mean
</th>
<th style="text-align:left;">
SD
</th>
<th style="text-align:left;">
N
</th>
<th style="text-align:left;">
Mean
</th>
<th style="text-align:left;">
SD
</th>
<th style="text-align:left;">
Test
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
age
</td>
<td style="text-align:left;">
226
</td>
<td style="text-align:left;">
51
</td>
<td style="text-align:left;">
14
</td>
<td style="text-align:left;">
486
</td>
<td style="text-align:left;">
54
</td>
<td style="text-align:left;">
17
</td>
<td style="text-align:left;">
F=5.528<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
female
</td>
<td style="text-align:left;">
226
</td>
<td style="text-align:left;">
0.3
</td>
<td style="text-align:left;">
0.46
</td>
<td style="text-align:left;">
486
</td>
<td style="text-align:left;">
0.45
</td>
<td style="text-align:left;">
0.5
</td>
<td style="text-align:left;">
F=14.238<sup>***</sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
education
</td>
<td style="text-align:left;">
226
</td>
<td style="text-align:left;">
3.7
</td>
<td style="text-align:left;">
6.4
</td>
<td style="text-align:left;">
486
</td>
<td style="text-align:left;">
3.8
</td>
<td style="text-align:left;">
7.6
</td>
<td style="text-align:left;">
F=0.045<sup></sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
income
</td>
<td style="text-align:left;">
226
</td>
<td style="text-align:left;">
7
</td>
<td style="text-align:left;">
3.4
</td>
<td style="text-align:left;">
486
</td>
<td style="text-align:left;">
6.8
</td>
<td style="text-align:left;">
3.1
</td>
<td style="text-align:left;">
F=0.253<sup></sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
psu_rul
</td>
<td style="text-align:left;">
226
</td>
<td style="text-align:left;">
0.35
</td>
<td style="text-align:left;">
0.48
</td>
<td style="text-align:left;">
486
</td>
<td style="text-align:left;">
0.32
</td>
<td style="text-align:left;">
0.47
</td>
<td style="text-align:left;">
F=0.568<sup></sup>
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; border:0;" colspan="100%">
<sup></sup> Statistical significance markers: * p&lt;0.1; ** p&lt;0.05;
*** p&lt;0.01
</td>
</tr>
</tfoot>
</table>
</div>
<div id="table-d3-covariate-distribution-before-entropy-balancing-the-first-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D3: Covariate distribution BEFORE entropy
balancing (The first gold medal case))</h3>
<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb26-1"><a href="#cb26-1" tabindex="-1"></a>pre_balance_stats_df <span class="ot">&lt;-</span> df_rdd48 <span class="sc">|&gt;</span></span>
<span id="cb26-2"><a href="#cb26-2" tabindex="-1"></a>  <span class="fu">summarise</span>(</span>
<span id="cb26-3"><a href="#cb26-3" tabindex="-1"></a>    <span class="at">Age_Mean_Control =</span> <span class="fu">mean</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-4"><a href="#cb26-4" tabindex="-1"></a>    <span class="at">Age_Mean_Treatment =</span> <span class="fu">mean</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-5"><a href="#cb26-5" tabindex="-1"></a>    <span class="at">Age_Variance_Control =</span> <span class="fu">var</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-6"><a href="#cb26-6" tabindex="-1"></a>    <span class="at">Age_Variance_Treatment =</span> <span class="fu">var</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-7"><a href="#cb26-7" tabindex="-1"></a>    <span class="at">Age_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-8"><a href="#cb26-8" tabindex="-1"></a>    <span class="at">Age_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-9"><a href="#cb26-9" tabindex="-1"></a>    </span>
<span id="cb26-10"><a href="#cb26-10" tabindex="-1"></a>    <span class="at">Education_Mean_Control =</span> <span class="fu">mean</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-11"><a href="#cb26-11" tabindex="-1"></a>    <span class="at">Education_Mean_Treatment =</span> <span class="fu">mean</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-12"><a href="#cb26-12" tabindex="-1"></a>    <span class="at">Education_Variance_Control =</span> <span class="fu">var</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-13"><a href="#cb26-13" tabindex="-1"></a>    <span class="at">Education_Variance_Treatment =</span> <span class="fu">var</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-14"><a href="#cb26-14" tabindex="-1"></a>    <span class="at">Education_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-15"><a href="#cb26-15" tabindex="-1"></a>    <span class="at">Education_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-16"><a href="#cb26-16" tabindex="-1"></a>    </span>
<span id="cb26-17"><a href="#cb26-17" tabindex="-1"></a>    <span class="at">Income_Mean_Control =</span> <span class="fu">mean</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-18"><a href="#cb26-18" tabindex="-1"></a>    <span class="at">Income_Mean_Treatment =</span> <span class="fu">mean</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-19"><a href="#cb26-19" tabindex="-1"></a>    <span class="at">Income_Variance_Control =</span> <span class="fu">var</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-20"><a href="#cb26-20" tabindex="-1"></a>    <span class="at">Income_Variance_Treatment =</span> <span class="fu">var</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-21"><a href="#cb26-21" tabindex="-1"></a>    <span class="at">Income_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-22"><a href="#cb26-22" tabindex="-1"></a>    <span class="at">Income_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-23"><a href="#cb26-23" tabindex="-1"></a>    </span>
<span id="cb26-24"><a href="#cb26-24" tabindex="-1"></a>    <span class="at">psu_rul_Mean_Control =</span> <span class="fu">mean</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-25"><a href="#cb26-25" tabindex="-1"></a>    <span class="at">psu_rul_Mean_Treatment =</span> <span class="fu">mean</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-26"><a href="#cb26-26" tabindex="-1"></a>    <span class="at">psu_rul_Variance_Control =</span> <span class="fu">var</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-27"><a href="#cb26-27" tabindex="-1"></a>    <span class="at">psu_rul_Variance_Treatment =</span> <span class="fu">var</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-28"><a href="#cb26-28" tabindex="-1"></a>    <span class="at">psu_rul_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-29"><a href="#cb26-29" tabindex="-1"></a>    <span class="at">psu_rul_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-30"><a href="#cb26-30" tabindex="-1"></a>    </span>
<span id="cb26-31"><a href="#cb26-31" tabindex="-1"></a>    <span class="at">patriotism_Mean_Control =</span> <span class="fu">mean</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-32"><a href="#cb26-32" tabindex="-1"></a>    <span class="at">patriotism_Mean_Treatment =</span> <span class="fu">mean</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-33"><a href="#cb26-33" tabindex="-1"></a>    <span class="at">patriotism_Variance_Control =</span> <span class="fu">var</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-34"><a href="#cb26-34" tabindex="-1"></a>    <span class="at">patriotism_Variance_Treatment =</span> <span class="fu">var</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-35"><a href="#cb26-35" tabindex="-1"></a>    <span class="at">patriotism_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb26-36"><a href="#cb26-36" tabindex="-1"></a>    <span class="at">patriotism_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb26-37"><a href="#cb26-37" tabindex="-1"></a>  )</span>
<span id="cb26-38"><a href="#cb26-38" tabindex="-1"></a></span>
<span id="cb26-39"><a href="#cb26-39" tabindex="-1"></a>pre_balance_stats_df_cleaned <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb26-40"><a href="#cb26-40" tabindex="-1"></a>  <span class="at">Variable =</span> <span class="fu">c</span>(<span class="st">&quot;Age&quot;</span>, <span class="st">&quot;Education&quot;</span>, <span class="st">&quot;Income&quot;</span>, <span class="st">&quot;psu_rul&quot;</span>, <span class="st">&quot;patriotism&quot;</span>),</span>
<span id="cb26-41"><a href="#cb26-41" tabindex="-1"></a>  <span class="at">Mean_Control =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Mean_Control, pre_balance_stats_df<span class="sc">$</span>Education_Mean_Control, pre_balance_stats_df<span class="sc">$</span>Income_Mean_Control, pre_balance_stats_df<span class="sc">$</span>psu_rul_Mean_Control, pre_balance_stats_df<span class="sc">$</span>patriotism_Mean_Control),</span>
<span id="cb26-42"><a href="#cb26-42" tabindex="-1"></a>  <span class="at">Variance_Control =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Variance_Control, pre_balance_stats_df<span class="sc">$</span>Education_Variance_Control, pre_balance_stats_df<span class="sc">$</span>Income_Variance_Control, pre_balance_stats_df<span class="sc">$</span>psu_rul_Variance_Control, pre_balance_stats_df<span class="sc">$</span>patriotism_Variance_Control),</span>
<span id="cb26-43"><a href="#cb26-43" tabindex="-1"></a>  <span class="at">Skewness_Control =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Skewness_Control, pre_balance_stats_df<span class="sc">$</span>Education_Skewness_Control, pre_balance_stats_df<span class="sc">$</span>Income_Skewness_Control, pre_balance_stats_df<span class="sc">$</span>psu_rul_Skewness_Control, pre_balance_stats_df<span class="sc">$</span>patriotism_Skewness_Control),</span>
<span id="cb26-44"><a href="#cb26-44" tabindex="-1"></a>  <span class="at">Mean_Treatment =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Mean_Treatment, pre_balance_stats_df<span class="sc">$</span>Education_Mean_Treatment, pre_balance_stats_df<span class="sc">$</span>Income_Mean_Treatment, pre_balance_stats_df<span class="sc">$</span>psu_rul_Mean_Treatment, pre_balance_stats_df<span class="sc">$</span>patriotism_Mean_Treatment),</span>
<span id="cb26-45"><a href="#cb26-45" tabindex="-1"></a>  <span class="at">Variance_Treatment =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Variance_Treatment, pre_balance_stats_df<span class="sc">$</span>Education_Variance_Treatment, pre_balance_stats_df<span class="sc">$</span>Income_Variance_Treatment, pre_balance_stats_df<span class="sc">$</span>psu_rul_Variance_Treatment, pre_balance_stats_df<span class="sc">$</span>patriotism_Variance_Treatment),</span>
<span id="cb26-46"><a href="#cb26-46" tabindex="-1"></a>  <span class="at">Skewness_Treatment =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Skewness_Treatment, pre_balance_stats_df<span class="sc">$</span>Education_Skewness_Treatment, pre_balance_stats_df<span class="sc">$</span>Income_Skewness_Treatment, pre_balance_stats_df<span class="sc">$</span>psu_rul_Skewness_Treatment, pre_balance_stats_df<span class="sc">$</span>patriotism_Skewness_Treatment)</span>
<span id="cb26-47"><a href="#cb26-47" tabindex="-1"></a>)</span>
<span id="cb26-48"><a href="#cb26-48" tabindex="-1"></a></span>
<span id="cb26-49"><a href="#cb26-49" tabindex="-1"></a><span class="fu">colnames</span>(pre_balance_stats_df_cleaned) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Variable&quot;</span>, </span>
<span id="cb26-50"><a href="#cb26-50" tabindex="-1"></a>                                            <span class="st">&quot;Mean (Control)&quot;</span>, </span>
<span id="cb26-51"><a href="#cb26-51" tabindex="-1"></a>                                            <span class="st">&quot;Variance (Control)&quot;</span>, </span>
<span id="cb26-52"><a href="#cb26-52" tabindex="-1"></a>                                            <span class="st">&quot;Skewness (Control)&quot;</span>,</span>
<span id="cb26-53"><a href="#cb26-53" tabindex="-1"></a>                                            <span class="st">&quot;Mean (Treatment)&quot;</span>,</span>
<span id="cb26-54"><a href="#cb26-54" tabindex="-1"></a>                                            <span class="st">&quot;Variance (Treatment)&quot;</span>,</span>
<span id="cb26-55"><a href="#cb26-55" tabindex="-1"></a>                                            <span class="st">&quot;Skewness (Treatment)&quot;</span>)</span>
<span id="cb26-56"><a href="#cb26-56" tabindex="-1"></a></span>
<span id="cb26-57"><a href="#cb26-57" tabindex="-1"></a><span class="fu">rownames</span>(pre_balance_stats_df_cleaned) <span class="ot">&lt;-</span> <span class="cn">NULL</span></span>
<span id="cb26-58"><a href="#cb26-58" tabindex="-1"></a></span>
<span id="cb26-59"><a href="#cb26-59" tabindex="-1"></a>pre_balance_stats_df_cleaned <span class="sc">|&gt;</span></span>
<span id="cb26-60"><a href="#cb26-60" tabindex="-1"></a>  <span class="fu">kable</span>(<span class="at">format =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb26-61"><a href="#cb26-61" tabindex="-1"></a>        <span class="at">caption =</span> <span class="st">&quot;&quot;</span>,</span>
<span id="cb26-62"><a href="#cb26-62" tabindex="-1"></a>        <span class="at">digits =</span> <span class="dv">3</span>, </span>
<span id="cb26-63"><a href="#cb26-63" tabindex="-1"></a>        <span class="at">booktabs =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb26-64"><a href="#cb26-64" tabindex="-1"></a>  <span class="fu">kable_styling</span>(<span class="at">bootstrap_options =</span> <span class="fu">c</span>(<span class="st">&quot;striped&quot;</span>, <span class="st">&quot;hover&quot;</span>, <span class="st">&quot;condensed&quot;</span>),</span>
<span id="cb26-65"><a href="#cb26-65" tabindex="-1"></a>                <span class="at">full_width =</span> <span class="cn">FALSE</span>,</span>
<span id="cb26-66"><a href="#cb26-66" tabindex="-1"></a>                <span class="at">position =</span> <span class="st">&quot;center&quot;</span>)<span class="sc">|&gt;</span> </span>
<span id="cb26-67"><a href="#cb26-67" tabindex="-1"></a>  <span class="fu">kable_classic</span>(<span class="at">full_width =</span> <span class="cn">FALSE</span>, <span class="at">html_font =</span> <span class="st">&quot;Times&quot;</span>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;">
Variable
</th>
<th style="text-align:right;">
Mean (Control)
</th>
<th style="text-align:right;">
Variance (Control)
</th>
<th style="text-align:right;">
Skewness (Control)
</th>
<th style="text-align:right;">
Mean (Treatment)
</th>
<th style="text-align:right;">
Variance (Treatment)
</th>
<th style="text-align:right;">
Skewness (Treatment)
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Age
</td>
<td style="text-align:right;">
49.846
</td>
<td style="text-align:right;">
186.680
</td>
<td style="text-align:right;">
-0.073
</td>
<td style="text-align:right;">
63.401
</td>
<td style="text-align:right;">
291.092
</td>
<td style="text-align:right;">
-1.696
</td>
</tr>
<tr>
<td style="text-align:left;">
Education
</td>
<td style="text-align:right;">
3.294
</td>
<td style="text-align:right;">
0.829
</td>
<td style="text-align:right;">
-0.810
</td>
<td style="text-align:right;">
2.984
</td>
<td style="text-align:right;">
0.845
</td>
<td style="text-align:right;">
-0.095
</td>
</tr>
<tr>
<td style="text-align:left;">
Income
</td>
<td style="text-align:right;">
7.183
</td>
<td style="text-align:right;">
10.861
</td>
<td style="text-align:right;">
0.269
</td>
<td style="text-align:right;">
6.022
</td>
<td style="text-align:right;">
7.988
</td>
<td style="text-align:right;">
0.825
</td>
</tr>
<tr>
<td style="text-align:left;">
psu_rul
</td>
<td style="text-align:right;">
0.301
</td>
<td style="text-align:right;">
0.211
</td>
<td style="text-align:right;">
0.865
</td>
<td style="text-align:right;">
0.319
</td>
<td style="text-align:right;">
0.218
</td>
<td style="text-align:right;">
0.772
</td>
</tr>
<tr>
<td style="text-align:left;">
patriotism
</td>
<td style="text-align:right;">
0.004
</td>
<td style="text-align:right;">
0.888
</td>
<td style="text-align:right;">
-0.888
</td>
<td style="text-align:right;">
0.184
</td>
<td style="text-align:right;">
0.622
</td>
<td style="text-align:right;">
-0.907
</td>
</tr>
</tbody>
</table>
</div>
<div id="table-d4-covariate-distribution-after-entropy-balancing-the-first-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D4: Covariate distribution AFTER entropy
balancing (The first gold medal case)</h3>
<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb27-1"><a href="#cb27-1" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> df_rdd48[, <span class="fu">c</span>(<span class="st">&quot;age&quot;</span>, <span class="st">&quot;female&quot;</span>, <span class="st">&quot;education&quot;</span>, <span class="st">&quot;income&quot;</span>, <span class="st">&quot;psu_rul&quot;</span>, <span class="st">&quot;patriotism&quot;</span>)]</span>
<span id="cb27-2"><a href="#cb27-2" tabindex="-1"></a></span>
<span id="cb27-3"><a href="#cb27-3" tabindex="-1"></a>treatment <span class="ot">&lt;-</span> df_rdd48<span class="sc">$</span>cutoff</span>
<span id="cb27-4"><a href="#cb27-4" tabindex="-1"></a></span>
<span id="cb27-5"><a href="#cb27-5" tabindex="-1"></a>eb_result <span class="ot">&lt;-</span> <span class="fu">weightit</span>(</span>
<span id="cb27-6"><a href="#cb27-6" tabindex="-1"></a>  cutoff <span class="sc">~</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul <span class="sc">+</span> patriotism,</span>
<span id="cb27-7"><a href="#cb27-7" tabindex="-1"></a>  <span class="at">data =</span> df_rdd48,</span>
<span id="cb27-8"><a href="#cb27-8" tabindex="-1"></a>  <span class="at">method =</span> <span class="st">&quot;ebal&quot;</span></span>
<span id="cb27-9"><a href="#cb27-9" tabindex="-1"></a>)</span>
<span id="cb27-10"><a href="#cb27-10" tabindex="-1"></a></span>
<span id="cb27-11"><a href="#cb27-11" tabindex="-1"></a></span>
<span id="cb27-12"><a href="#cb27-12" tabindex="-1"></a>weights <span class="ot">&lt;-</span> eb_result<span class="sc">$</span>w</span>
<span id="cb27-13"><a href="#cb27-13" tabindex="-1"></a></span>
<span id="cb27-14"><a href="#cb27-14" tabindex="-1"></a></span>
<span id="cb27-15"><a href="#cb27-15" tabindex="-1"></a>weights <span class="ot">&lt;-</span> eb_result<span class="sc">$</span>w</span>
<span id="cb27-16"><a href="#cb27-16" tabindex="-1"></a><span class="fu">summary</span>(eb_result<span class="sc">$</span>w)</span></code></pre></div>
<pre><code>##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.1360  0.6409  0.8987  1.0000  1.2225 12.0208</code></pre>
<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb29-1"><a href="#cb29-1" tabindex="-1"></a>weighted_df_rdd48 <span class="ot">&lt;-</span> <span class="fu">na.omit</span>(df_rdd48)</span>
<span id="cb29-2"><a href="#cb29-2" tabindex="-1"></a>weighted_df_rdd48<span class="sc">$</span>weights <span class="ot">&lt;-</span> weights</span>
<span id="cb29-3"><a href="#cb29-3" tabindex="-1"></a></span>
<span id="cb29-4"><a href="#cb29-4" tabindex="-1"></a>final_stats_df <span class="ot">&lt;-</span> weighted_df_rdd48 <span class="sc">|&gt;</span></span>
<span id="cb29-5"><a href="#cb29-5" tabindex="-1"></a>  <span class="fu">summarise</span>(</span>
<span id="cb29-6"><a href="#cb29-6" tabindex="-1"></a>    <span class="at">Age_Mean_Control =</span> <span class="fu">weighted.mean</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-7"><a href="#cb29-7" tabindex="-1"></a>    <span class="at">Age_Mean_Treatment =</span> <span class="fu">weighted.mean</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-8"><a href="#cb29-8" tabindex="-1"></a>    <span class="at">Age_Variance_Control =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-9"><a href="#cb29-9" tabindex="-1"></a>    <span class="at">Age_Variance_Treatment =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-10"><a href="#cb29-10" tabindex="-1"></a>    <span class="at">Age_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-11"><a href="#cb29-11" tabindex="-1"></a>    <span class="at">Age_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-12"><a href="#cb29-12" tabindex="-1"></a>    </span>
<span id="cb29-13"><a href="#cb29-13" tabindex="-1"></a>    <span class="at">Education_Mean_Control =</span> <span class="fu">weighted.mean</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-14"><a href="#cb29-14" tabindex="-1"></a>    <span class="at">Education_Mean_Treatment =</span> <span class="fu">weighted.mean</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-15"><a href="#cb29-15" tabindex="-1"></a>    <span class="at">Education_Variance_Control =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-16"><a href="#cb29-16" tabindex="-1"></a>    <span class="at">Education_Variance_Treatment =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-17"><a href="#cb29-17" tabindex="-1"></a>    <span class="at">Education_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-18"><a href="#cb29-18" tabindex="-1"></a>    <span class="at">Education_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-19"><a href="#cb29-19" tabindex="-1"></a>    </span>
<span id="cb29-20"><a href="#cb29-20" tabindex="-1"></a>    <span class="at">Income_Mean_Control =</span> <span class="fu">weighted.mean</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-21"><a href="#cb29-21" tabindex="-1"></a>    <span class="at">Income_Mean_Treatment =</span> <span class="fu">weighted.mean</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-22"><a href="#cb29-22" tabindex="-1"></a>    <span class="at">Income_Variance_Control =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-23"><a href="#cb29-23" tabindex="-1"></a>    <span class="at">Income_Variance_Treatment =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-24"><a href="#cb29-24" tabindex="-1"></a>    <span class="at">Income_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-25"><a href="#cb29-25" tabindex="-1"></a>    <span class="at">Income_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-26"><a href="#cb29-26" tabindex="-1"></a>    </span>
<span id="cb29-27"><a href="#cb29-27" tabindex="-1"></a>    <span class="at">psu_rul_Mean_Control =</span> <span class="fu">weighted.mean</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-28"><a href="#cb29-28" tabindex="-1"></a>    <span class="at">psu_rul_Mean_Treatment =</span> <span class="fu">weighted.mean</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-29"><a href="#cb29-29" tabindex="-1"></a>    <span class="at">psu_rul_Variance_Control =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-30"><a href="#cb29-30" tabindex="-1"></a>    <span class="at">psu_rul_Variance_Treatment =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-31"><a href="#cb29-31" tabindex="-1"></a>    <span class="at">psu_rul_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-32"><a href="#cb29-32" tabindex="-1"></a>    <span class="at">psu_rul_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-33"><a href="#cb29-33" tabindex="-1"></a>    </span>
<span id="cb29-34"><a href="#cb29-34" tabindex="-1"></a>    <span class="at">patriotism_Mean_Control =</span> <span class="fu">weighted.mean</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-35"><a href="#cb29-35" tabindex="-1"></a>    <span class="at">patriotism_Mean_Treatment =</span> <span class="fu">weighted.mean</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-36"><a href="#cb29-36" tabindex="-1"></a>    <span class="at">patriotism_Variance_Control =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-37"><a href="#cb29-37" tabindex="-1"></a>    <span class="at">patriotism_Variance_Treatment =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-38"><a href="#cb29-38" tabindex="-1"></a>    <span class="at">patriotism_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb29-39"><a href="#cb29-39" tabindex="-1"></a>    <span class="at">patriotism_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb29-40"><a href="#cb29-40" tabindex="-1"></a>  )</span>
<span id="cb29-41"><a href="#cb29-41" tabindex="-1"></a></span>
<span id="cb29-42"><a href="#cb29-42" tabindex="-1"></a>final_stats_df_cleaned <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb29-43"><a href="#cb29-43" tabindex="-1"></a>  <span class="at">Variable =</span> <span class="fu">c</span>(<span class="st">&quot;Age&quot;</span>, <span class="st">&quot;Education&quot;</span>, <span class="st">&quot;Income&quot;</span>, <span class="st">&quot;In-partisans&quot;</span>, <span class="st">&quot;patriotism&quot;</span>),</span>
<span id="cb29-44"><a href="#cb29-44" tabindex="-1"></a>  <span class="at">Mean_Control =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Mean_Control, final_stats_df<span class="sc">$</span>Education_Mean_Control, final_stats_df<span class="sc">$</span>Income_Mean_Control, final_stats_df<span class="sc">$</span>psu_rul_Mean_Control, final_stats_df<span class="sc">$</span>patriotism_Mean_Control),</span>
<span id="cb29-45"><a href="#cb29-45" tabindex="-1"></a>  <span class="at">Variance_Control =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Variance_Control, final_stats_df<span class="sc">$</span>Education_Variance_Control, final_stats_df<span class="sc">$</span>Income_Variance_Control, final_stats_df<span class="sc">$</span>psu_rul_Variance_Control, final_stats_df<span class="sc">$</span>patriotism_Variance_Control),</span>
<span id="cb29-46"><a href="#cb29-46" tabindex="-1"></a>  <span class="at">Skewness_Control =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Skewness_Control, final_stats_df<span class="sc">$</span>Education_Skewness_Control, final_stats_df<span class="sc">$</span>Income_Skewness_Control, final_stats_df<span class="sc">$</span>psu_rul_Skewness_Control, final_stats_df<span class="sc">$</span>patriotism_Skewness_Control),</span>
<span id="cb29-47"><a href="#cb29-47" tabindex="-1"></a>  <span class="at">Mean_Treatment =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Mean_Treatment, final_stats_df<span class="sc">$</span>Education_Mean_Treatment, final_stats_df<span class="sc">$</span>Income_Mean_Treatment, final_stats_df<span class="sc">$</span>psu_rul_Mean_Treatment, final_stats_df<span class="sc">$</span>patriotism_Mean_Treatment),</span>
<span id="cb29-48"><a href="#cb29-48" tabindex="-1"></a>  <span class="at">Variance_Treatment =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Variance_Treatment, final_stats_df<span class="sc">$</span>Education_Variance_Treatment, final_stats_df<span class="sc">$</span>Income_Variance_Treatment, final_stats_df<span class="sc">$</span>psu_rul_Variance_Treatment, final_stats_df<span class="sc">$</span>patriotism_Variance_Treatment),</span>
<span id="cb29-49"><a href="#cb29-49" tabindex="-1"></a>  <span class="at">Skewness_Treatment =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Skewness_Treatment, final_stats_df<span class="sc">$</span>Education_Skewness_Treatment, final_stats_df<span class="sc">$</span>Income_Skewness_Treatment, final_stats_df<span class="sc">$</span>psu_rul_Skewness_Treatment, final_stats_df<span class="sc">$</span>patriotism_Skewness_Treatment)</span>
<span id="cb29-50"><a href="#cb29-50" tabindex="-1"></a>)</span>
<span id="cb29-51"><a href="#cb29-51" tabindex="-1"></a></span>
<span id="cb29-52"><a href="#cb29-52" tabindex="-1"></a><span class="fu">colnames</span>(final_stats_df_cleaned) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Variable&quot;</span>, </span>
<span id="cb29-53"><a href="#cb29-53" tabindex="-1"></a>                                      <span class="st">&quot;Mean &quot;</span>, </span>
<span id="cb29-54"><a href="#cb29-54" tabindex="-1"></a>                                      <span class="st">&quot;Variance&quot;</span>, </span>
<span id="cb29-55"><a href="#cb29-55" tabindex="-1"></a>                                      <span class="st">&quot;Skewness &quot;</span>,</span>
<span id="cb29-56"><a href="#cb29-56" tabindex="-1"></a>                                      <span class="st">&quot;Mean &quot;</span>,</span>
<span id="cb29-57"><a href="#cb29-57" tabindex="-1"></a>                                      <span class="st">&quot;Variance &quot;</span>,</span>
<span id="cb29-58"><a href="#cb29-58" tabindex="-1"></a>                                      <span class="st">&quot;Skewness &quot;</span>)</span>
<span id="cb29-59"><a href="#cb29-59" tabindex="-1"></a></span>
<span id="cb29-60"><a href="#cb29-60" tabindex="-1"></a><span class="fu">rownames</span>(final_stats_df_cleaned) <span class="ot">&lt;-</span> <span class="cn">NULL</span></span>
<span id="cb29-61"><a href="#cb29-61" tabindex="-1"></a></span>
<span id="cb29-62"><a href="#cb29-62" tabindex="-1"></a>final_stats_df_cleaned <span class="sc">|&gt;</span></span>
<span id="cb29-63"><a href="#cb29-63" tabindex="-1"></a>  <span class="fu">kable</span>(<span class="at">format =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb29-64"><a href="#cb29-64" tabindex="-1"></a>        <span class="at">caption =</span> <span class="st">&quot;&quot;</span>,</span>
<span id="cb29-65"><a href="#cb29-65" tabindex="-1"></a>        <span class="at">digits =</span> <span class="dv">3</span>,</span>
<span id="cb29-66"><a href="#cb29-66" tabindex="-1"></a>        <span class="at">booktabs =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb29-67"><a href="#cb29-67" tabindex="-1"></a>  <span class="fu">kable_styling</span>(<span class="at">bootstrap_options =</span> <span class="fu">c</span>(<span class="st">&quot;striped&quot;</span>, <span class="st">&quot;hover&quot;</span>, <span class="st">&quot;condensed&quot;</span>),</span>
<span id="cb29-68"><a href="#cb29-68" tabindex="-1"></a>                <span class="at">full_width =</span> <span class="cn">FALSE</span>,</span>
<span id="cb29-69"><a href="#cb29-69" tabindex="-1"></a>                <span class="at">position =</span> <span class="st">&quot;center&quot;</span>)<span class="sc">|&gt;</span> </span>
<span id="cb29-70"><a href="#cb29-70" tabindex="-1"></a>  <span class="fu">kable_classic</span>(<span class="at">full_width =</span> <span class="cn">FALSE</span>, <span class="at">html_font =</span> <span class="st">&quot;Times&quot;</span>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;">
Variable
</th>
<th style="text-align:right;">
Mean
</th>
<th style="text-align:right;">
Variance
</th>
<th style="text-align:right;">
Skewness
</th>
<th style="text-align:right;">
Mean
</th>
<th style="text-align:right;">
Variance
</th>
<th style="text-align:right;">
Skewness
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Age
</td>
<td style="text-align:right;">
53.180
</td>
<td style="text-align:right;">
184.663
</td>
<td style="text-align:right;">
-0.073
</td>
<td style="text-align:right;">
53.180
</td>
<td style="text-align:right;">
482.544
</td>
<td style="text-align:right;">
-1.696
</td>
</tr>
<tr>
<td style="text-align:left;">
Education
</td>
<td style="text-align:right;">
3.218
</td>
<td style="text-align:right;">
0.839
</td>
<td style="text-align:right;">
-0.810
</td>
<td style="text-align:right;">
3.218
</td>
<td style="text-align:right;">
0.867
</td>
<td style="text-align:right;">
-0.095
</td>
</tr>
<tr>
<td style="text-align:left;">
Income
</td>
<td style="text-align:right;">
6.897
</td>
<td style="text-align:right;">
10.408
</td>
<td style="text-align:right;">
0.269
</td>
<td style="text-align:right;">
6.897
</td>
<td style="text-align:right;">
11.597
</td>
<td style="text-align:right;">
0.825
</td>
</tr>
<tr>
<td style="text-align:left;">
In-partisans
</td>
<td style="text-align:right;">
0.305
</td>
<td style="text-align:right;">
0.213
</td>
<td style="text-align:right;">
0.865
</td>
<td style="text-align:right;">
0.305
</td>
<td style="text-align:right;">
0.213
</td>
<td style="text-align:right;">
0.772
</td>
</tr>
<tr>
<td style="text-align:left;">
patriotism
</td>
<td style="text-align:right;">
0.049
</td>
<td style="text-align:right;">
0.838
</td>
<td style="text-align:right;">
-0.888
</td>
<td style="text-align:right;">
0.049
</td>
<td style="text-align:right;">
0.745
</td>
<td style="text-align:right;">
-0.907
</td>
</tr>
</tbody>
</table>
</div>
<div id="table-d5-covariate-distribution-before-entropy-balancing-the-second-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D5: Covariate distribution BEFORE entropy
balancing (The second gold medal case)</h3>
<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb30-1"><a href="#cb30-1" tabindex="-1"></a><span class="co">#before balancing</span></span>
<span id="cb30-2"><a href="#cb30-2" tabindex="-1"></a>pre_balance_stats_df <span class="ot">&lt;-</span> df_rdd66 <span class="sc">|&gt;</span></span>
<span id="cb30-3"><a href="#cb30-3" tabindex="-1"></a>  <span class="fu">summarise</span>(</span>
<span id="cb30-4"><a href="#cb30-4" tabindex="-1"></a>    <span class="at">Age_Mean_Control =</span> <span class="fu">mean</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-5"><a href="#cb30-5" tabindex="-1"></a>    <span class="at">Age_Mean_Treatment =</span> <span class="fu">mean</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-6"><a href="#cb30-6" tabindex="-1"></a>    <span class="at">Age_Variance_Control =</span> <span class="fu">var</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-7"><a href="#cb30-7" tabindex="-1"></a>    <span class="at">Age_Variance_Treatment =</span> <span class="fu">var</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-8"><a href="#cb30-8" tabindex="-1"></a>    <span class="at">Age_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-9"><a href="#cb30-9" tabindex="-1"></a>    <span class="at">Age_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-10"><a href="#cb30-10" tabindex="-1"></a>    </span>
<span id="cb30-11"><a href="#cb30-11" tabindex="-1"></a>    <span class="at">Education_Mean_Control =</span> <span class="fu">mean</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-12"><a href="#cb30-12" tabindex="-1"></a>    <span class="at">Education_Mean_Treatment =</span> <span class="fu">mean</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-13"><a href="#cb30-13" tabindex="-1"></a>    <span class="at">Education_Variance_Control =</span> <span class="fu">var</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-14"><a href="#cb30-14" tabindex="-1"></a>    <span class="at">Education_Variance_Treatment =</span> <span class="fu">var</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-15"><a href="#cb30-15" tabindex="-1"></a>    <span class="at">Education_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-16"><a href="#cb30-16" tabindex="-1"></a>    <span class="at">Education_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-17"><a href="#cb30-17" tabindex="-1"></a>    </span>
<span id="cb30-18"><a href="#cb30-18" tabindex="-1"></a>    <span class="at">Income_Mean_Control =</span> <span class="fu">mean</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-19"><a href="#cb30-19" tabindex="-1"></a>    <span class="at">Income_Mean_Treatment =</span> <span class="fu">mean</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-20"><a href="#cb30-20" tabindex="-1"></a>    <span class="at">Income_Variance_Control =</span> <span class="fu">var</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-21"><a href="#cb30-21" tabindex="-1"></a>    <span class="at">Income_Variance_Treatment =</span> <span class="fu">var</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-22"><a href="#cb30-22" tabindex="-1"></a>    <span class="at">Income_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-23"><a href="#cb30-23" tabindex="-1"></a>    <span class="at">Income_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-24"><a href="#cb30-24" tabindex="-1"></a>    </span>
<span id="cb30-25"><a href="#cb30-25" tabindex="-1"></a>    <span class="at">psu_rul_Mean_Control =</span> <span class="fu">mean</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-26"><a href="#cb30-26" tabindex="-1"></a>    <span class="at">psu_rul_Mean_Treatment =</span> <span class="fu">mean</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-27"><a href="#cb30-27" tabindex="-1"></a>    <span class="at">psu_rul_Variance_Control =</span> <span class="fu">var</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-28"><a href="#cb30-28" tabindex="-1"></a>    <span class="at">psu_rul_Variance_Treatment =</span> <span class="fu">var</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-29"><a href="#cb30-29" tabindex="-1"></a>    <span class="at">psu_rul_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-30"><a href="#cb30-30" tabindex="-1"></a>    <span class="at">psu_rul_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-31"><a href="#cb30-31" tabindex="-1"></a>    </span>
<span id="cb30-32"><a href="#cb30-32" tabindex="-1"></a>    <span class="at">patriotism_Mean_Control =</span> <span class="fu">mean</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-33"><a href="#cb30-33" tabindex="-1"></a>    <span class="at">patriotism_Mean_Treatment =</span> <span class="fu">mean</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-34"><a href="#cb30-34" tabindex="-1"></a>    <span class="at">patriotism_Variance_Control =</span> <span class="fu">var</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-35"><a href="#cb30-35" tabindex="-1"></a>    <span class="at">patriotism_Variance_Treatment =</span> <span class="fu">var</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-36"><a href="#cb30-36" tabindex="-1"></a>    <span class="at">patriotism_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb30-37"><a href="#cb30-37" tabindex="-1"></a>    <span class="at">patriotism_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb30-38"><a href="#cb30-38" tabindex="-1"></a>  )</span>
<span id="cb30-39"><a href="#cb30-39" tabindex="-1"></a></span>
<span id="cb30-40"><a href="#cb30-40" tabindex="-1"></a>pre_balance_stats_df_cleaned <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb30-41"><a href="#cb30-41" tabindex="-1"></a>  <span class="at">Variable =</span> <span class="fu">c</span>(<span class="st">&quot;Age&quot;</span>, <span class="st">&quot;Education&quot;</span>, <span class="st">&quot;Income&quot;</span>, <span class="st">&quot;psu_rul&quot;</span>, <span class="st">&quot;patriotism&quot;</span>),</span>
<span id="cb30-42"><a href="#cb30-42" tabindex="-1"></a>  <span class="at">Mean_Control =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Mean_Control, pre_balance_stats_df<span class="sc">$</span>Education_Mean_Control, pre_balance_stats_df<span class="sc">$</span>Income_Mean_Control, pre_balance_stats_df<span class="sc">$</span>psu_rul_Mean_Control, pre_balance_stats_df<span class="sc">$</span>patriotism_Mean_Control),</span>
<span id="cb30-43"><a href="#cb30-43" tabindex="-1"></a>  <span class="at">Variance_Control =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Variance_Control, pre_balance_stats_df<span class="sc">$</span>Education_Variance_Control, pre_balance_stats_df<span class="sc">$</span>Income_Variance_Control, pre_balance_stats_df<span class="sc">$</span>psu_rul_Variance_Control, pre_balance_stats_df<span class="sc">$</span>patriotism_Variance_Control),</span>
<span id="cb30-44"><a href="#cb30-44" tabindex="-1"></a>  <span class="at">Skewness_Control =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Skewness_Control, pre_balance_stats_df<span class="sc">$</span>Education_Skewness_Control, pre_balance_stats_df<span class="sc">$</span>Income_Skewness_Control, pre_balance_stats_df<span class="sc">$</span>psu_rul_Skewness_Control, pre_balance_stats_df<span class="sc">$</span>patriotism_Skewness_Control),</span>
<span id="cb30-45"><a href="#cb30-45" tabindex="-1"></a>  <span class="at">Mean_Treatment =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Mean_Treatment, pre_balance_stats_df<span class="sc">$</span>Education_Mean_Treatment, pre_balance_stats_df<span class="sc">$</span>Income_Mean_Treatment, pre_balance_stats_df<span class="sc">$</span>psu_rul_Mean_Treatment, pre_balance_stats_df<span class="sc">$</span>patriotism_Mean_Treatment),</span>
<span id="cb30-46"><a href="#cb30-46" tabindex="-1"></a>  <span class="at">Variance_Treatment =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Variance_Treatment, pre_balance_stats_df<span class="sc">$</span>Education_Variance_Treatment, pre_balance_stats_df<span class="sc">$</span>Income_Variance_Treatment, pre_balance_stats_df<span class="sc">$</span>psu_rul_Variance_Treatment, pre_balance_stats_df<span class="sc">$</span>patriotism_Variance_Treatment),</span>
<span id="cb30-47"><a href="#cb30-47" tabindex="-1"></a>  <span class="at">Skewness_Treatment =</span> <span class="fu">c</span>(pre_balance_stats_df<span class="sc">$</span>Age_Skewness_Treatment, pre_balance_stats_df<span class="sc">$</span>Education_Skewness_Treatment, pre_balance_stats_df<span class="sc">$</span>Income_Skewness_Treatment, pre_balance_stats_df<span class="sc">$</span>psu_rul_Skewness_Treatment, pre_balance_stats_df<span class="sc">$</span>patriotism_Skewness_Treatment)</span>
<span id="cb30-48"><a href="#cb30-48" tabindex="-1"></a>)</span>
<span id="cb30-49"><a href="#cb30-49" tabindex="-1"></a></span>
<span id="cb30-50"><a href="#cb30-50" tabindex="-1"></a><span class="fu">colnames</span>(pre_balance_stats_df_cleaned) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Variable&quot;</span>, </span>
<span id="cb30-51"><a href="#cb30-51" tabindex="-1"></a>                                            <span class="st">&quot;Mean (Control)&quot;</span>, </span>
<span id="cb30-52"><a href="#cb30-52" tabindex="-1"></a>                                            <span class="st">&quot;Variance (Control)&quot;</span>, </span>
<span id="cb30-53"><a href="#cb30-53" tabindex="-1"></a>                                            <span class="st">&quot;Skewness (Control)&quot;</span>,</span>
<span id="cb30-54"><a href="#cb30-54" tabindex="-1"></a>                                            <span class="st">&quot;Mean (Treatment)&quot;</span>,</span>
<span id="cb30-55"><a href="#cb30-55" tabindex="-1"></a>                                            <span class="st">&quot;Variance (Treatment)&quot;</span>,</span>
<span id="cb30-56"><a href="#cb30-56" tabindex="-1"></a>                                            <span class="st">&quot;Skewness (Treatment)&quot;</span>)</span>
<span id="cb30-57"><a href="#cb30-57" tabindex="-1"></a></span>
<span id="cb30-58"><a href="#cb30-58" tabindex="-1"></a><span class="fu">rownames</span>(pre_balance_stats_df_cleaned) <span class="ot">&lt;-</span> <span class="cn">NULL</span></span>
<span id="cb30-59"><a href="#cb30-59" tabindex="-1"></a></span>
<span id="cb30-60"><a href="#cb30-60" tabindex="-1"></a>pre_balance_stats_df_cleaned <span class="sc">|&gt;</span></span>
<span id="cb30-61"><a href="#cb30-61" tabindex="-1"></a>  <span class="fu">kable</span>(<span class="at">format =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb30-62"><a href="#cb30-62" tabindex="-1"></a>        <span class="at">caption =</span> <span class="st">&quot;&quot;</span>,</span>
<span id="cb30-63"><a href="#cb30-63" tabindex="-1"></a>        <span class="at">digits =</span> <span class="dv">3</span>,  </span>
<span id="cb30-64"><a href="#cb30-64" tabindex="-1"></a>        <span class="at">booktabs =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb30-65"><a href="#cb30-65" tabindex="-1"></a>  <span class="fu">kable_styling</span>(<span class="at">bootstrap_options =</span> <span class="fu">c</span>(<span class="st">&quot;striped&quot;</span>, <span class="st">&quot;hover&quot;</span>, <span class="st">&quot;condensed&quot;</span>),</span>
<span id="cb30-66"><a href="#cb30-66" tabindex="-1"></a>                <span class="at">full_width =</span> <span class="cn">FALSE</span>,</span>
<span id="cb30-67"><a href="#cb30-67" tabindex="-1"></a>                <span class="at">position =</span> <span class="st">&quot;center&quot;</span>)<span class="sc">|&gt;</span> </span>
<span id="cb30-68"><a href="#cb30-68" tabindex="-1"></a>  <span class="fu">kable_classic</span>(<span class="at">full_width =</span> <span class="cn">FALSE</span>, <span class="at">html_font =</span> <span class="st">&quot;Times&quot;</span>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;">
Variable
</th>
<th style="text-align:right;">
Mean (Control)
</th>
<th style="text-align:right;">
Variance (Control)
</th>
<th style="text-align:right;">
Skewness (Control)
</th>
<th style="text-align:right;">
Mean (Treatment)
</th>
<th style="text-align:right;">
Variance (Treatment)
</th>
<th style="text-align:right;">
Skewness (Treatment)
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Age
</td>
<td style="text-align:right;">
50.770
</td>
<td style="text-align:right;">
183.902
</td>
<td style="text-align:right;">
-0.161
</td>
<td style="text-align:right;">
53.747
</td>
<td style="text-align:right;">
276.758
</td>
<td style="text-align:right;">
-0.356
</td>
</tr>
<tr>
<td style="text-align:left;">
Education
</td>
<td style="text-align:right;">
3.650
</td>
<td style="text-align:right;">
41.411
</td>
<td style="text-align:right;">
14.388
</td>
<td style="text-align:right;">
3.774
</td>
<td style="text-align:right;">
57.301
</td>
<td style="text-align:right;">
12.284
</td>
</tr>
<tr>
<td style="text-align:left;">
Income
</td>
<td style="text-align:right;">
6.969
</td>
<td style="text-align:right;">
11.532
</td>
<td style="text-align:right;">
0.365
</td>
<td style="text-align:right;">
6.840
</td>
<td style="text-align:right;">
9.653
</td>
<td style="text-align:right;">
0.435
</td>
</tr>
<tr>
<td style="text-align:left;">
psu_rul
</td>
<td style="text-align:right;">
0.350
</td>
<td style="text-align:right;">
0.228
</td>
<td style="text-align:right;">
0.627
</td>
<td style="text-align:right;">
0.321
</td>
<td style="text-align:right;">
0.218
</td>
<td style="text-align:right;">
0.765
</td>
</tr>
<tr>
<td style="text-align:left;">
patriotism
</td>
<td style="text-align:right;">
-0.015
</td>
<td style="text-align:right;">
0.862
</td>
<td style="text-align:right;">
-0.714
</td>
<td style="text-align:right;">
0.007
</td>
<td style="text-align:right;">
0.835
</td>
<td style="text-align:right;">
-0.791
</td>
</tr>
</tbody>
</table>
</div>
<div id="table-d6-covariate-distribution-after-entropy-balancing-the-second-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D6: Covariate distribution AFTER entropy
balancing (The second gold medal case)</h3>
<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb31-1"><a href="#cb31-1" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> df_rdd66[, <span class="fu">c</span>(<span class="st">&quot;age&quot;</span>, <span class="st">&quot;female&quot;</span>, <span class="st">&quot;education&quot;</span>, <span class="st">&quot;income&quot;</span>, <span class="st">&quot;psu_rul&quot;</span>, <span class="st">&quot;patriotism&quot;</span>)]</span>
<span id="cb31-2"><a href="#cb31-2" tabindex="-1"></a></span>
<span id="cb31-3"><a href="#cb31-3" tabindex="-1"></a>treatment <span class="ot">&lt;-</span> df_rdd66<span class="sc">$</span>cutoff</span>
<span id="cb31-4"><a href="#cb31-4" tabindex="-1"></a></span>
<span id="cb31-5"><a href="#cb31-5" tabindex="-1"></a>eb_result <span class="ot">&lt;-</span> <span class="fu">weightit</span>(</span>
<span id="cb31-6"><a href="#cb31-6" tabindex="-1"></a>  cutoff <span class="sc">~</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul <span class="sc">+</span> patriotism,</span>
<span id="cb31-7"><a href="#cb31-7" tabindex="-1"></a>  <span class="at">data =</span> df_rdd66,</span>
<span id="cb31-8"><a href="#cb31-8" tabindex="-1"></a>  <span class="at">method =</span> <span class="st">&quot;ebal&quot;</span></span>
<span id="cb31-9"><a href="#cb31-9" tabindex="-1"></a>)</span>
<span id="cb31-10"><a href="#cb31-10" tabindex="-1"></a></span>
<span id="cb31-11"><a href="#cb31-11" tabindex="-1"></a></span>
<span id="cb31-12"><a href="#cb31-12" tabindex="-1"></a>weights <span class="ot">&lt;-</span> eb_result<span class="sc">$</span>w</span>
<span id="cb31-13"><a href="#cb31-13" tabindex="-1"></a></span>
<span id="cb31-14"><a href="#cb31-14" tabindex="-1"></a></span>
<span id="cb31-15"><a href="#cb31-15" tabindex="-1"></a>weighted_df_rdd66 <span class="ot">&lt;-</span> <span class="fu">na.omit</span>(df_rdd66)</span>
<span id="cb31-16"><a href="#cb31-16" tabindex="-1"></a>weighted_df_rdd66<span class="sc">$</span>weights <span class="ot">&lt;-</span> weights</span>
<span id="cb31-17"><a href="#cb31-17" tabindex="-1"></a></span>
<span id="cb31-18"><a href="#cb31-18" tabindex="-1"></a></span>
<span id="cb31-19"><a href="#cb31-19" tabindex="-1"></a></span>
<span id="cb31-20"><a href="#cb31-20" tabindex="-1"></a>final_stats_df <span class="ot">&lt;-</span> weighted_df_rdd66 <span class="sc">|&gt;</span></span>
<span id="cb31-21"><a href="#cb31-21" tabindex="-1"></a>  <span class="fu">summarise</span>(</span>
<span id="cb31-22"><a href="#cb31-22" tabindex="-1"></a>    <span class="at">Age_Mean_Control =</span> <span class="fu">weighted.mean</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-23"><a href="#cb31-23" tabindex="-1"></a>    <span class="at">Age_Mean_Treatment =</span> <span class="fu">weighted.mean</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-24"><a href="#cb31-24" tabindex="-1"></a>    <span class="at">Age_Variance_Control =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-25"><a href="#cb31-25" tabindex="-1"></a>    <span class="at">Age_Variance_Treatment =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-26"><a href="#cb31-26" tabindex="-1"></a>    <span class="at">Age_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(age[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-27"><a href="#cb31-27" tabindex="-1"></a>    <span class="at">Age_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(age[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-28"><a href="#cb31-28" tabindex="-1"></a>    </span>
<span id="cb31-29"><a href="#cb31-29" tabindex="-1"></a>    <span class="at">Education_Mean_Control =</span> <span class="fu">weighted.mean</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-30"><a href="#cb31-30" tabindex="-1"></a>    <span class="at">Education_Mean_Treatment =</span> <span class="fu">weighted.mean</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-31"><a href="#cb31-31" tabindex="-1"></a>    <span class="at">Education_Variance_Control =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-32"><a href="#cb31-32" tabindex="-1"></a>    <span class="at">Education_Variance_Treatment =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-33"><a href="#cb31-33" tabindex="-1"></a>    <span class="at">Education_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(education[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-34"><a href="#cb31-34" tabindex="-1"></a>    <span class="at">Education_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(education[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-35"><a href="#cb31-35" tabindex="-1"></a>    </span>
<span id="cb31-36"><a href="#cb31-36" tabindex="-1"></a>    <span class="at">Income_Mean_Control =</span> <span class="fu">weighted.mean</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-37"><a href="#cb31-37" tabindex="-1"></a>    <span class="at">Income_Mean_Treatment =</span> <span class="fu">weighted.mean</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-38"><a href="#cb31-38" tabindex="-1"></a>    <span class="at">Income_Variance_Control =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-39"><a href="#cb31-39" tabindex="-1"></a>    <span class="at">Income_Variance_Treatment =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-40"><a href="#cb31-40" tabindex="-1"></a>    <span class="at">Income_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(income[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-41"><a href="#cb31-41" tabindex="-1"></a>    <span class="at">Income_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(income[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-42"><a href="#cb31-42" tabindex="-1"></a>    </span>
<span id="cb31-43"><a href="#cb31-43" tabindex="-1"></a>    <span class="at">psu_rul_Mean_Control =</span> <span class="fu">weighted.mean</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-44"><a href="#cb31-44" tabindex="-1"></a>    <span class="at">psu_rul_Mean_Treatment =</span> <span class="fu">weighted.mean</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-45"><a href="#cb31-45" tabindex="-1"></a>    <span class="at">psu_rul_Variance_Control =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-46"><a href="#cb31-46" tabindex="-1"></a>    <span class="at">psu_rul_Variance_Treatment =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-47"><a href="#cb31-47" tabindex="-1"></a>    <span class="at">psu_rul_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-48"><a href="#cb31-48" tabindex="-1"></a>    <span class="at">psu_rul_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(psu_rul[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-49"><a href="#cb31-49" tabindex="-1"></a>    </span>
<span id="cb31-50"><a href="#cb31-50" tabindex="-1"></a>    <span class="at">Partriotism_Mean_Control =</span> <span class="fu">weighted.mean</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-51"><a href="#cb31-51" tabindex="-1"></a>    <span class="at">Partriotism_Mean_Treatment =</span> <span class="fu">weighted.mean</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-52"><a href="#cb31-52" tabindex="-1"></a>    <span class="at">Partriotism_Variance_Control =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], weights[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-53"><a href="#cb31-53" tabindex="-1"></a>    <span class="at">Partriotism_Variance_Treatment =</span> Hmisc<span class="sc">::</span><span class="fu">wtd.var</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], weights[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-54"><a href="#cb31-54" tabindex="-1"></a>    <span class="at">Partriotism_Skewness_Control =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">0</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb31-55"><a href="#cb31-55" tabindex="-1"></a>    <span class="at">Partriotism_Skewness_Treatment =</span> e1071<span class="sc">::</span><span class="fu">skewness</span>(patriotism[cutoff <span class="sc">==</span> <span class="dv">1</span>], <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb31-56"><a href="#cb31-56" tabindex="-1"></a>  )</span>
<span id="cb31-57"><a href="#cb31-57" tabindex="-1"></a></span>
<span id="cb31-58"><a href="#cb31-58" tabindex="-1"></a>final_stats_df_cleaned <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb31-59"><a href="#cb31-59" tabindex="-1"></a>  <span class="at">Variable =</span> <span class="fu">c</span>(<span class="st">&quot;Age&quot;</span>, <span class="st">&quot;Education&quot;</span>, <span class="st">&quot;Income&quot;</span>, <span class="st">&quot;psu_rul&quot;</span>, <span class="st">&quot;Partriotism&quot;</span>),</span>
<span id="cb31-60"><a href="#cb31-60" tabindex="-1"></a>  <span class="at">Mean_Control =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Mean_Control, final_stats_df<span class="sc">$</span>Education_Mean_Control, final_stats_df<span class="sc">$</span>Income_Mean_Control, final_stats_df<span class="sc">$</span>psu_rul_Mean_Control, final_stats_df<span class="sc">$</span>Partriotism_Mean_Control),</span>
<span id="cb31-61"><a href="#cb31-61" tabindex="-1"></a>  <span class="at">Variance_Control =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Variance_Control, final_stats_df<span class="sc">$</span>Education_Variance_Control, final_stats_df<span class="sc">$</span>Income_Variance_Control, final_stats_df<span class="sc">$</span>psu_rul_Variance_Control, final_stats_df<span class="sc">$</span>Partriotism_Variance_Control),</span>
<span id="cb31-62"><a href="#cb31-62" tabindex="-1"></a>  <span class="at">Skewness_Control =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Skewness_Control, final_stats_df<span class="sc">$</span>Education_Skewness_Control, final_stats_df<span class="sc">$</span>Income_Skewness_Control, final_stats_df<span class="sc">$</span>psu_rul_Skewness_Control, final_stats_df<span class="sc">$</span>Partriotism_Skewness_Control),</span>
<span id="cb31-63"><a href="#cb31-63" tabindex="-1"></a>  <span class="at">Mean_Treatment =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Mean_Treatment, final_stats_df<span class="sc">$</span>Education_Mean_Treatment, final_stats_df<span class="sc">$</span>Income_Mean_Treatment, final_stats_df<span class="sc">$</span>psu_rul_Mean_Treatment, final_stats_df<span class="sc">$</span>Partriotism_Mean_Treatment),</span>
<span id="cb31-64"><a href="#cb31-64" tabindex="-1"></a>  <span class="at">Variance_Treatment =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Variance_Treatment, final_stats_df<span class="sc">$</span>Education_Variance_Treatment, final_stats_df<span class="sc">$</span>Income_Variance_Treatment, final_stats_df<span class="sc">$</span>psu_rul_Variance_Treatment, final_stats_df<span class="sc">$</span>Partriotism_Variance_Treatment),</span>
<span id="cb31-65"><a href="#cb31-65" tabindex="-1"></a>  <span class="at">Skewness_Treatment =</span> <span class="fu">c</span>(final_stats_df<span class="sc">$</span>Age_Skewness_Treatment, final_stats_df<span class="sc">$</span>Education_Skewness_Treatment, final_stats_df<span class="sc">$</span>Income_Skewness_Treatment, final_stats_df<span class="sc">$</span>psu_rul_Skewness_Treatment, final_stats_df<span class="sc">$</span>Partriotism_Skewness_Treatment)</span>
<span id="cb31-66"><a href="#cb31-66" tabindex="-1"></a>)</span>
<span id="cb31-67"><a href="#cb31-67" tabindex="-1"></a></span>
<span id="cb31-68"><a href="#cb31-68" tabindex="-1"></a><span class="fu">colnames</span>(final_stats_df_cleaned) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Variable&quot;</span>, </span>
<span id="cb31-69"><a href="#cb31-69" tabindex="-1"></a>                                      <span class="st">&quot;Mean &quot;</span>, </span>
<span id="cb31-70"><a href="#cb31-70" tabindex="-1"></a>                                      <span class="st">&quot;Variance&quot;</span>, </span>
<span id="cb31-71"><a href="#cb31-71" tabindex="-1"></a>                                      <span class="st">&quot;Skewness &quot;</span>,</span>
<span id="cb31-72"><a href="#cb31-72" tabindex="-1"></a>                                      <span class="st">&quot;Mean &quot;</span>,</span>
<span id="cb31-73"><a href="#cb31-73" tabindex="-1"></a>                                      <span class="st">&quot;Variance &quot;</span>,</span>
<span id="cb31-74"><a href="#cb31-74" tabindex="-1"></a>                                      <span class="st">&quot;Skewness &quot;</span>)</span>
<span id="cb31-75"><a href="#cb31-75" tabindex="-1"></a></span>
<span id="cb31-76"><a href="#cb31-76" tabindex="-1"></a><span class="fu">rownames</span>(final_stats_df_cleaned) <span class="ot">&lt;-</span> <span class="cn">NULL</span></span>
<span id="cb31-77"><a href="#cb31-77" tabindex="-1"></a></span>
<span id="cb31-78"><a href="#cb31-78" tabindex="-1"></a>final_stats_df_cleaned <span class="sc">|&gt;</span></span>
<span id="cb31-79"><a href="#cb31-79" tabindex="-1"></a>  <span class="fu">kable</span>(<span class="at">format =</span> <span class="st">&quot;html&quot;</span>,</span>
<span id="cb31-80"><a href="#cb31-80" tabindex="-1"></a>        <span class="at">caption =</span> <span class="st">&quot;&quot;</span>,</span>
<span id="cb31-81"><a href="#cb31-81" tabindex="-1"></a>        <span class="at">digits =</span> <span class="dv">3</span>,</span>
<span id="cb31-82"><a href="#cb31-82" tabindex="-1"></a>        <span class="at">booktabs =</span> <span class="cn">TRUE</span>) <span class="sc">|&gt;</span></span>
<span id="cb31-83"><a href="#cb31-83" tabindex="-1"></a>  <span class="fu">kable_styling</span>(<span class="at">bootstrap_options =</span> <span class="fu">c</span>(<span class="st">&quot;striped&quot;</span>, <span class="st">&quot;hover&quot;</span>, <span class="st">&quot;condensed&quot;</span>),</span>
<span id="cb31-84"><a href="#cb31-84" tabindex="-1"></a>                <span class="at">full_width =</span> <span class="cn">FALSE</span>,</span>
<span id="cb31-85"><a href="#cb31-85" tabindex="-1"></a>                <span class="at">position =</span> <span class="st">&quot;center&quot;</span>)<span class="sc">|&gt;</span> </span>
<span id="cb31-86"><a href="#cb31-86" tabindex="-1"></a>  <span class="fu">kable_classic</span>(<span class="at">full_width =</span> <span class="cn">FALSE</span>, <span class="at">html_font =</span> <span class="st">&quot;Times&quot;</span>) </span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;">
Variable
</th>
<th style="text-align:right;">
Mean
</th>
<th style="text-align:right;">
Variance
</th>
<th style="text-align:right;">
Skewness
</th>
<th style="text-align:right;">
Mean
</th>
<th style="text-align:right;">
Variance
</th>
<th style="text-align:right;">
Skewness
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Age
</td>
<td style="text-align:right;">
52.802
</td>
<td style="text-align:right;">
177.853
</td>
<td style="text-align:right;">
-0.161
</td>
<td style="text-align:right;">
52.802
</td>
<td style="text-align:right;">
274.868
</td>
<td style="text-align:right;">
-0.356
</td>
</tr>
<tr>
<td style="text-align:left;">
Education
</td>
<td style="text-align:right;">
3.735
</td>
<td style="text-align:right;">
55.827
</td>
<td style="text-align:right;">
14.388
</td>
<td style="text-align:right;">
3.735
</td>
<td style="text-align:right;">
52.710
</td>
<td style="text-align:right;">
12.284
</td>
</tr>
<tr>
<td style="text-align:left;">
Income
</td>
<td style="text-align:right;">
6.881
</td>
<td style="text-align:right;">
11.328
</td>
<td style="text-align:right;">
0.365
</td>
<td style="text-align:right;">
6.881
</td>
<td style="text-align:right;">
9.788
</td>
<td style="text-align:right;">
0.435
</td>
</tr>
<tr>
<td style="text-align:left;">
psu_rul
</td>
<td style="text-align:right;">
0.330
</td>
<td style="text-align:right;">
0.222
</td>
<td style="text-align:right;">
0.627
</td>
<td style="text-align:right;">
0.330
</td>
<td style="text-align:right;">
0.222
</td>
<td style="text-align:right;">
0.765
</td>
</tr>
<tr>
<td style="text-align:left;">
Partriotism
</td>
<td style="text-align:right;">
0.000
</td>
<td style="text-align:right;">
0.881
</td>
<td style="text-align:right;">
-0.714
</td>
<td style="text-align:right;">
0.000
</td>
<td style="text-align:right;">
0.840
</td>
<td style="text-align:right;">
-0.791
</td>
</tr>
</tbody>
</table>
</div>
<div id="table-d7-regression-results-with-and-without-weights-the-first-gold-meda-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D7: Regression Results with and without
Weights (The first gold meda case)</h3>
<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb32-1"><a href="#cb32-1" tabindex="-1"></a>model1 <span class="ot">&lt;-</span> <span class="fu">lm</span>(cabap <span class="sc">~</span> cutoff <span class="sc">+</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul <span class="sc">+</span> patriotism,</span>
<span id="cb32-2"><a href="#cb32-2" tabindex="-1"></a>             <span class="at">data =</span> df_rdd48)</span>
<span id="cb32-3"><a href="#cb32-3" tabindex="-1"></a></span>
<span id="cb32-4"><a href="#cb32-4" tabindex="-1"></a>model2<span class="ot">&lt;-</span> <span class="fu">lm</span>(ftj <span class="sc">~</span> cutoff <span class="sc">+</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul <span class="sc">+</span> patriotism,</span>
<span id="cb32-5"><a href="#cb32-5" tabindex="-1"></a>            <span class="at">data =</span> df_rdd48)</span>
<span id="cb32-6"><a href="#cb32-6" tabindex="-1"></a></span>
<span id="cb32-7"><a href="#cb32-7" tabindex="-1"></a>model3 <span class="ot">&lt;-</span> <span class="fu">lm</span>(cabap <span class="sc">~</span> cutoff <span class="sc">+</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul <span class="sc">+</span> patriotism,</span>
<span id="cb32-8"><a href="#cb32-8" tabindex="-1"></a>             <span class="at">data =</span> weighted_df_rdd48, <span class="at">weights =</span> weights)</span>
<span id="cb32-9"><a href="#cb32-9" tabindex="-1"></a></span>
<span id="cb32-10"><a href="#cb32-10" tabindex="-1"></a>model4 <span class="ot">&lt;-</span> <span class="fu">lm</span>(ftj <span class="sc">~</span> cutoff <span class="sc">+</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul <span class="sc">+</span> patriotism,</span>
<span id="cb32-11"><a href="#cb32-11" tabindex="-1"></a>             <span class="at">data =</span> weighted_df_rdd48, <span class="at">weights =</span> weights)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Cabinet approval&lt;br&gt;No Weight
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Feeling thermometer&lt;br&gt;No Weight
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Cabinet approval&lt;br&gt;Weighted
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Feeling thermometer&lt;br&gt;Weighted
</div>
</th>
</tr>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="4">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
dep_header
</div>
</th>
</tr>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
Cabinet approval<br>No Weight
</th>
<th style="text-align:center;font-weight: bold;">
Feeling thermometer<br>No Weight
</th>
<th style="text-align:center;font-weight: bold;">
Cabinet approval<br>Weighted
</th>
<th style="text-align:center;font-weight: bold;">
Feeling thermometer<br>Weighted
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Treatment<br> (Before=0; After=1)
</td>
<td style="text-align:center;">
0.021<br>(0.037)
</td>
<td style="text-align:center;">
1.240<br>(2.281)
</td>
<td style="text-align:center;">
0.031<br>(0.031)
</td>
<td style="text-align:center;">
1.020<br>(1.967)
</td>
</tr>
<tr>
<td style="text-align:left;">
Age
</td>
<td style="text-align:center;">
-0.002<br>(0.001)
</td>
<td style="text-align:center;">
0.084<br>(0.059)
</td>
<td style="text-align:center;">
-0.002<br>(0.001)
</td>
<td style="text-align:center;">
0.075<br>(0.055)
</td>
</tr>
<tr>
<td style="text-align:left;">
Female<br> (Female=1; Male=0)
</td>
<td style="text-align:center;">
0.024<br>(0.030)
</td>
<td style="text-align:center;">
-1.921<br>(1.866)
</td>
<td style="text-align:center;">
0.012<br>(0.029)
</td>
<td style="text-align:center;">
-2.687<br>(1.802)
</td>
</tr>
<tr>
<td style="text-align:left;">
Education<br> (4-scales)
</td>
<td style="text-align:center;">
0.013<br>(0.016)
</td>
<td style="text-align:center;">
0.654<br>(0.961)
</td>
<td style="text-align:center;">
0.009<br>(0.016)
</td>
<td style="text-align:center;">
0.453<br>(0.981)
</td>
</tr>
<tr>
<td style="text-align:left;">
Income<br> (20-scales)
</td>
<td style="text-align:center;">
0.001<br>(0.004)
</td>
<td style="text-align:center;">
-0.023<br>(0.271)
</td>
<td style="text-align:center;">
0.002<br>(0.004)
</td>
<td style="text-align:center;">
-0.050<br>(0.273)
</td>
</tr>
<tr>
<td style="text-align:left;">
In-partisans<br> (In-partisan=1; Otherwise=0)
</td>
<td style="text-align:center;">
0.454***<br>(0.030)
</td>
<td style="text-align:center;">
24.078***<br>(1.843)
</td>
<td style="text-align:center;">
0.478***<br>(0.030)
</td>
<td style="text-align:center;">
26.079***<br>(1.870)
</td>
</tr>
<tr>
<td style="text-align:left;">
Patriotism<br> (Factor scores)
</td>
<td style="text-align:center;">
0.025<br>(0.015)
</td>
<td style="text-align:center;">
5.557***<br>(0.939)
</td>
<td style="text-align:center;">
0.026<br>(0.015)
</td>
<td style="text-align:center;">
4.937***<br>(0.951)
</td>
</tr>
<tr>
<td style="text-align:left;">
Constant
</td>
<td style="text-align:center;">
0.121<br>(0.076)
</td>
<td style="text-align:center;">
27.227***<br>(4.722)
</td>
<td style="text-align:center;">
0.125<br>(0.076)
</td>
<td style="text-align:center;">
28.289***<br>(4.796)
</td>
</tr>
<tr>
<td style="text-align:left;">
Weighted
</td>
<td style="text-align:center;">
NO
</td>
<td style="text-align:center;">
NO
</td>
<td style="text-align:center;">
YES
</td>
<td style="text-align:center;">
YES
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.258
</td>
<td style="text-align:center;">
0.252
</td>
<td style="text-align:center;">
0.282
</td>
<td style="text-align:center;">
0.256
</td>
</tr>
<tr>
<td style="text-align:left;">
Adjusted <span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.251
</td>
<td style="text-align:center;">
0.245
</td>
<td style="text-align:center;">
0.275
</td>
<td style="text-align:center;">
0.249
</td>
</tr>
<tr>
<td style="text-align:left;">
Residual Std. Error
</td>
<td style="text-align:center;">
0.368
</td>
<td style="text-align:center;">
22.714
</td>
<td style="text-align:center;">
0.365
</td>
<td style="text-align:center;">
23.040
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 732)
</td>
<td style="text-align:center;">
(df = 732)
</td>
<td style="text-align:center;">
(df = 732)
</td>
<td style="text-align:center;">
(df = 732)
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(F\)</span> Statistic
</td>
<td style="text-align:center;">
36.425
</td>
<td style="text-align:center;">
35.178
</td>
<td style="text-align:center;">
41.104
</td>
<td style="text-align:center;">
35.962
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 7; 732)
</td>
<td style="text-align:center;">
(df = 7; 732)
</td>
<td style="text-align:center;">
(df = 7; 732)
</td>
<td style="text-align:center;">
(df = 7; 732)
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Standard errors in parentheses.
</td>
</tr>
</tfoot>
</table>
</div>
<div id="table-d8-regression-results-with-and-without-weights-the-second-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D8: Regression Results with and without
Weights (The second gold medal case)</h3>
<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb33-1"><a href="#cb33-1" tabindex="-1"></a>model1 <span class="ot">&lt;-</span> <span class="fu">lm</span>(cabap <span class="sc">~</span> cutoff <span class="sc">+</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul <span class="sc">+</span> patriotism,</span>
<span id="cb33-2"><a href="#cb33-2" tabindex="-1"></a>             <span class="at">data =</span> df_rdd66)</span>
<span id="cb33-3"><a href="#cb33-3" tabindex="-1"></a></span>
<span id="cb33-4"><a href="#cb33-4" tabindex="-1"></a>model2<span class="ot">&lt;-</span> <span class="fu">lm</span>(ftj <span class="sc">~</span> cutoff <span class="sc">+</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul <span class="sc">+</span> patriotism,</span>
<span id="cb33-5"><a href="#cb33-5" tabindex="-1"></a>            <span class="at">data =</span> df_rdd66)</span>
<span id="cb33-6"><a href="#cb33-6" tabindex="-1"></a></span>
<span id="cb33-7"><a href="#cb33-7" tabindex="-1"></a>model3 <span class="ot">&lt;-</span> <span class="fu">lm</span>(cabap <span class="sc">~</span> cutoff <span class="sc">+</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul <span class="sc">+</span> patriotism,</span>
<span id="cb33-8"><a href="#cb33-8" tabindex="-1"></a>             <span class="at">data =</span> weighted_df_rdd66, <span class="at">weights =</span> weights)</span>
<span id="cb33-9"><a href="#cb33-9" tabindex="-1"></a></span>
<span id="cb33-10"><a href="#cb33-10" tabindex="-1"></a>model4 <span class="ot">&lt;-</span> <span class="fu">lm</span>(ftj <span class="sc">~</span> cutoff <span class="sc">+</span> age <span class="sc">+</span> female <span class="sc">+</span> education <span class="sc">+</span> income <span class="sc">+</span> psu_rul <span class="sc">+</span> patriotism,</span>
<span id="cb33-11"><a href="#cb33-11" tabindex="-1"></a>             <span class="at">data =</span> weighted_df_rdd66, <span class="at">weights =</span> weights)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Cabinet approval&lt;br&gt;No Weight
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Feeling thermometer&lt;br&gt;No Weight
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Cabinet approval&lt;br&gt;Weighted
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Feeling thermometer&lt;br&gt;Weighted
</div>
</th>
</tr>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="4">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
dep_header
</div>
</th>
</tr>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
Cabinet approval<br>No Weight
</th>
<th style="text-align:center;font-weight: bold;">
Feeling thermometer<br>No Weight
</th>
<th style="text-align:center;font-weight: bold;">
Cabinet approval<br>Weighted
</th>
<th style="text-align:center;font-weight: bold;">
Feeling thermometer<br>Weighted
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Treatment<br> (Before=0; After=1)
</td>
<td style="text-align:center;">
0.072*<br>(0.029)
</td>
<td style="text-align:center;">
0.502<br>(1.819)
</td>
<td style="text-align:center;">
0.067*<br>(0.029)
</td>
<td style="text-align:center;">
0.441<br>(1.803)
</td>
</tr>
<tr>
<td style="text-align:left;">
Age
</td>
<td style="text-align:center;">
-0.000<br>(0.001)
</td>
<td style="text-align:center;">
0.045<br>(0.055)
</td>
<td style="text-align:center;">
-0.000<br>(0.001)
</td>
<td style="text-align:center;">
0.034<br>(0.055)
</td>
</tr>
<tr>
<td style="text-align:left;">
Female<br> (Female=1; Male=0)
</td>
<td style="text-align:center;">
-0.024<br>(0.028)
</td>
<td style="text-align:center;">
-0.683<br>(1.729)
</td>
<td style="text-align:center;">
-0.027<br>(0.027)
</td>
<td style="text-align:center;">
-0.751<br>(1.721)
</td>
</tr>
<tr>
<td style="text-align:left;">
Education<br> (4-scales)
</td>
<td style="text-align:center;">
-0.000<br>(0.002)
</td>
<td style="text-align:center;">
0.050<br>(0.116)
</td>
<td style="text-align:center;">
-0.001<br>(0.002)
</td>
<td style="text-align:center;">
0.097<br>(0.115)
</td>
</tr>
<tr>
<td style="text-align:left;">
Income<br> (20-scales)
</td>
<td style="text-align:center;">
-0.004<br>(0.004)
</td>
<td style="text-align:center;">
-0.133<br>(0.264)
</td>
<td style="text-align:center;">
-0.005<br>(0.004)
</td>
<td style="text-align:center;">
-0.087<br>(0.265)
</td>
</tr>
<tr>
<td style="text-align:left;">
In-partisans<br> (In-partisan=1; Otherwise=0)
</td>
<td style="text-align:center;">
0.475***<br>(0.029)
</td>
<td style="text-align:center;">
24.235***<br>(1.795)
</td>
<td style="text-align:center;">
0.479***<br>(0.029)
</td>
<td style="text-align:center;">
24.240***<br>(1.807)
</td>
</tr>
<tr>
<td style="text-align:left;">
Patriotism<br> (Factor scores)
</td>
<td style="text-align:center;">
0.012<br>(0.015)
</td>
<td style="text-align:center;">
3.455***<br>(0.937)
</td>
<td style="text-align:center;">
0.010<br>(0.015)
</td>
<td style="text-align:center;">
3.647***<br>(0.935)
</td>
</tr>
<tr>
<td style="text-align:left;">
Constant
</td>
<td style="text-align:center;">
0.076<br>(0.064)
</td>
<td style="text-align:center;">
29.281***<br>(4.004)
</td>
<td style="text-align:center;">
0.090<br>(0.066)
</td>
<td style="text-align:center;">
29.472***<br>(4.133)
</td>
</tr>
<tr>
<td style="text-align:left;">
Weighted
</td>
<td style="text-align:center;">
NO
</td>
<td style="text-align:center;">
NO
</td>
<td style="text-align:center;">
YES
</td>
<td style="text-align:center;">
YES
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.290
</td>
<td style="text-align:center;">
0.230
</td>
<td style="text-align:center;">
0.294
</td>
<td style="text-align:center;">
0.231
</td>
</tr>
<tr>
<td style="text-align:left;">
Adjusted <span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.283
</td>
<td style="text-align:center;">
0.222
</td>
<td style="text-align:center;">
0.287
</td>
<td style="text-align:center;">
0.224
</td>
</tr>
<tr>
<td style="text-align:left;">
Residual Std. Error
</td>
<td style="text-align:center;">
0.355
</td>
<td style="text-align:center;">
22.270
</td>
<td style="text-align:center;">
0.355
</td>
<td style="text-align:center;">
22.396
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 704)
</td>
<td style="text-align:center;">
(df = 704)
</td>
<td style="text-align:center;">
(df = 704)
</td>
<td style="text-align:center;">
(df = 704)
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(F\)</span> Statistic
</td>
<td style="text-align:center;">
41.054
</td>
<td style="text-align:center;">
30.066
</td>
<td style="text-align:center;">
41.933
</td>
<td style="text-align:center;">
30.241
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 7; 704)
</td>
<td style="text-align:center;">
(df = 7; 704)
</td>
<td style="text-align:center;">
(df = 7; 704)
</td>
<td style="text-align:center;">
(df = 7; 704)
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Standard errors in parentheses.
</td>
</tr>
</tfoot>
</table>
</div>
<div id="table-d9-counterfactual-analysis-with-causal-forest-shows-the-null-effect-from-event-the-first-gold-medal" class="section level3 unnumbered">
<h3 class="unnumbered">Table D9: Counterfactual analysis with causal
forest shows the null effect from event (The first gold medal)</h3>
<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb34-1"><a href="#cb34-1" tabindex="-1"></a><span class="co">#cabinet approval</span></span>
<span id="cb34-2"><a href="#cb34-2" tabindex="-1"></a></span>
<span id="cb34-3"><a href="#cb34-3" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">1234</span>)</span>
<span id="cb34-4"><a href="#cb34-4" tabindex="-1"></a></span>
<span id="cb34-5"><a href="#cb34-5" tabindex="-1"></a>cf <span class="ot">&lt;-</span> <span class="fu">causal_forest</span>(</span>
<span id="cb34-6"><a href="#cb34-6" tabindex="-1"></a>  <span class="at">X =</span> df_rdd48[, <span class="fu">c</span>(<span class="st">&quot;age&quot;</span>,<span class="st">&quot;female&quot;</span>,<span class="st">&quot;education&quot;</span>,<span class="st">&quot;income&quot;</span>,<span class="st">&quot;psu_rul&quot;</span>)],</span>
<span id="cb34-7"><a href="#cb34-7" tabindex="-1"></a>  <span class="at">Y =</span> df_rdd48<span class="sc">$</span>cabap,</span>
<span id="cb34-8"><a href="#cb34-8" tabindex="-1"></a>  <span class="at">W =</span> df_rdd48<span class="sc">$</span>cutoff,</span>
<span id="cb34-9"><a href="#cb34-9" tabindex="-1"></a>  <span class="at">seed =</span> <span class="dv">2025</span></span>
<span id="cb34-10"><a href="#cb34-10" tabindex="-1"></a>)</span>
<span id="cb34-11"><a href="#cb34-11" tabindex="-1"></a></span>
<span id="cb34-12"><a href="#cb34-12" tabindex="-1"></a></span>
<span id="cb34-13"><a href="#cb34-13" tabindex="-1"></a>treatment_effects_cabap <span class="ot">&lt;-</span> <span class="fu">predict</span>(cf)<span class="sc">$</span>predictions</span>
<span id="cb34-14"><a href="#cb34-14" tabindex="-1"></a></span>
<span id="cb34-15"><a href="#cb34-15" tabindex="-1"></a>mean_effect_cabap <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">mean</span>(treatment_effects_cabap), </span>
<span id="cb34-16"><a href="#cb34-16" tabindex="-1"></a>                       <span class="fu">quantile</span>(treatment_effects_cabap, <span class="fl">0.025</span>), </span>
<span id="cb34-17"><a href="#cb34-17" tabindex="-1"></a>                       <span class="fu">quantile</span>(treatment_effects_cabap, <span class="fl">0.975</span>))</span>
<span id="cb34-18"><a href="#cb34-18" tabindex="-1"></a></span>
<span id="cb34-19"><a href="#cb34-19" tabindex="-1"></a><span class="co">#feeling thermometer</span></span>
<span id="cb34-20"><a href="#cb34-20" tabindex="-1"></a></span>
<span id="cb34-21"><a href="#cb34-21" tabindex="-1"></a></span>
<span id="cb34-22"><a href="#cb34-22" tabindex="-1"></a>cf_ftj <span class="ot">&lt;-</span> <span class="fu">causal_forest</span>(</span>
<span id="cb34-23"><a href="#cb34-23" tabindex="-1"></a>  <span class="at">X =</span> df_rdd48[, <span class="fu">c</span>(<span class="st">&quot;age&quot;</span>,<span class="st">&quot;female&quot;</span>,<span class="st">&quot;education&quot;</span>,<span class="st">&quot;income&quot;</span>,<span class="st">&quot;psu_rul&quot;</span>)],</span>
<span id="cb34-24"><a href="#cb34-24" tabindex="-1"></a>  <span class="at">Y =</span> df_rdd48<span class="sc">$</span>ftj,</span>
<span id="cb34-25"><a href="#cb34-25" tabindex="-1"></a>  <span class="at">W =</span> df_rdd48<span class="sc">$</span>cutoff,</span>
<span id="cb34-26"><a href="#cb34-26" tabindex="-1"></a>  <span class="at">seed =</span> <span class="dv">2025</span>)</span>
<span id="cb34-27"><a href="#cb34-27" tabindex="-1"></a></span>
<span id="cb34-28"><a href="#cb34-28" tabindex="-1"></a></span>
<span id="cb34-29"><a href="#cb34-29" tabindex="-1"></a>treatment_effects_ftj <span class="ot">&lt;-</span> <span class="fu">predict</span>(cf_ftj)<span class="sc">$</span>predictions</span>
<span id="cb34-30"><a href="#cb34-30" tabindex="-1"></a></span>
<span id="cb34-31"><a href="#cb34-31" tabindex="-1"></a>mean_effect_ftj <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">mean</span>(treatment_effects_ftj), </span>
<span id="cb34-32"><a href="#cb34-32" tabindex="-1"></a>                     <span class="fu">quantile</span>(treatment_effects_ftj, <span class="fl">0.025</span>), </span>
<span id="cb34-33"><a href="#cb34-33" tabindex="-1"></a>                     <span class="fu">quantile</span>(treatment_effects_ftj, <span class="fl">0.975</span>))</span>
<span id="cb34-34"><a href="#cb34-34" tabindex="-1"></a></span>
<span id="cb34-35"><a href="#cb34-35" tabindex="-1"></a></span>
<span id="cb34-36"><a href="#cb34-36" tabindex="-1"></a>df_cf <span class="ot">&lt;-</span> <span class="fu">t</span>(<span class="fu">cbind</span>(mean_effect_cabap, mean_effect_ftj))</span>
<span id="cb34-37"><a href="#cb34-37" tabindex="-1"></a><span class="fu">colnames</span>(df_cf) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;mean&quot;</span>, <span class="st">&quot;up&quot;</span>, <span class="st">&quot;low&quot;</span>)</span>
<span id="cb34-38"><a href="#cb34-38" tabindex="-1"></a></span>
<span id="cb34-39"><a href="#cb34-39" tabindex="-1"></a>df_cf <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">outcome =</span> <span class="fu">c</span>(<span class="st">&quot;mean_effect_cabap&quot;</span>, <span class="st">&quot;mean_effect_ftj&quot;</span>),df_cf)</span>
<span id="cb34-40"><a href="#cb34-40" tabindex="-1"></a></span>
<span id="cb34-41"><a href="#cb34-41" tabindex="-1"></a>df_cf<span class="sc">$</span>mean <span class="ot">&lt;-</span> <span class="fu">round</span>(df_cf<span class="sc">$</span>mean, <span class="dv">3</span>)</span>
<span id="cb34-42"><a href="#cb34-42" tabindex="-1"></a>df_cf<span class="sc">$</span>up <span class="ot">&lt;-</span> <span class="fu">round</span>(df_cf<span class="sc">$</span>up, <span class="dv">3</span>)</span>
<span id="cb34-43"><a href="#cb34-43" tabindex="-1"></a>df_cf<span class="sc">$</span>low <span class="ot">&lt;-</span> <span class="fu">round</span>(df_cf<span class="sc">$</span>low, <span class="dv">3</span>)</span>
<span id="cb34-44"><a href="#cb34-44" tabindex="-1"></a></span>
<span id="cb34-45"><a href="#cb34-45" tabindex="-1"></a>format_ci <span class="ot">&lt;-</span> <span class="cf">function</span>(mean, low, up) {</span>
<span id="cb34-46"><a href="#cb34-46" tabindex="-1"></a>  <span class="fu">sprintf</span>(<span class="st">&quot;%.3f</span><span class="sc">\n</span><span class="st">[%.3f, %.3f]&quot;</span>, mean, low, up)</span>
<span id="cb34-47"><a href="#cb34-47" tabindex="-1"></a>}</span>
<span id="cb34-48"><a href="#cb34-48" tabindex="-1"></a></span>
<span id="cb34-49"><a href="#cb34-49" tabindex="-1"></a>formatted_results <span class="ot">&lt;-</span> <span class="fu">mapply</span>(format_ci, df_cf<span class="sc">$</span>mean, df_cf<span class="sc">$</span>low, df_cf<span class="sc">$</span>up)</span>
<span id="cb34-50"><a href="#cb34-50" tabindex="-1"></a></span>
<span id="cb34-51"><a href="#cb34-51" tabindex="-1"></a>df_formatted <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb34-52"><a href="#cb34-52" tabindex="-1"></a>  <span class="at">Outcome =</span> df_cf<span class="sc">$</span>outcome,</span>
<span id="cb34-53"><a href="#cb34-53" tabindex="-1"></a>  <span class="at">Results =</span> formatted_results</span>
<span id="cb34-54"><a href="#cb34-54" tabindex="-1"></a>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;">
Outcome variables
</th>
<th style="text-align:center;">
Results
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Cabinet approval
</td>
<td style="text-align:center;">
0.017 [0.106, -0.077]
</td>
</tr>
<tr>
<td style="text-align:left;">
Feeling thermometer
</td>
<td style="text-align:center;">
-1.417 [6.330, -5.728]
</td>
</tr>
</tbody>
</table>
</div>
<div id="table-d10-counterfactual-analysis-with-causal-forest-also-shows-the-null-effect-from-event-the-second-gold-medal" class="section level3 unnumbered">
<h3 class="unnumbered">Table D10: Counterfactual analysis with causal
forest also shows the null effect from event (The second gold
medal)</h3>
<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb35-1"><a href="#cb35-1" tabindex="-1"></a><span class="fu">set.seed</span>(<span class="dv">1234</span>)</span>
<span id="cb35-2"><a href="#cb35-2" tabindex="-1"></a></span>
<span id="cb35-3"><a href="#cb35-3" tabindex="-1"></a><span class="co">#cabinet approval</span></span>
<span id="cb35-4"><a href="#cb35-4" tabindex="-1"></a>cf <span class="ot">&lt;-</span> <span class="fu">causal_forest</span>(</span>
<span id="cb35-5"><a href="#cb35-5" tabindex="-1"></a>  <span class="at">X =</span> df_rdd66[, <span class="fu">c</span>(<span class="st">&quot;age&quot;</span>,<span class="st">&quot;female&quot;</span>,<span class="st">&quot;education&quot;</span>,<span class="st">&quot;income&quot;</span>,<span class="st">&quot;psu_rul&quot;</span>)],</span>
<span id="cb35-6"><a href="#cb35-6" tabindex="-1"></a>  <span class="at">Y =</span> df_rdd66<span class="sc">$</span>cabap,</span>
<span id="cb35-7"><a href="#cb35-7" tabindex="-1"></a>  <span class="at">W =</span> df_rdd66<span class="sc">$</span>cutoff,</span>
<span id="cb35-8"><a href="#cb35-8" tabindex="-1"></a>  <span class="at">seed =</span> <span class="dv">2025</span></span>
<span id="cb35-9"><a href="#cb35-9" tabindex="-1"></a>)</span>
<span id="cb35-10"><a href="#cb35-10" tabindex="-1"></a></span>
<span id="cb35-11"><a href="#cb35-11" tabindex="-1"></a>treatment_effects_cabap <span class="ot">&lt;-</span> <span class="fu">predict</span>(cf)<span class="sc">$</span>predictions</span>
<span id="cb35-12"><a href="#cb35-12" tabindex="-1"></a></span>
<span id="cb35-13"><a href="#cb35-13" tabindex="-1"></a>mean_effect_cabap <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">mean</span>(treatment_effects_cabap), </span>
<span id="cb35-14"><a href="#cb35-14" tabindex="-1"></a>                       <span class="fu">quantile</span>(treatment_effects_cabap, <span class="fl">0.025</span>), </span>
<span id="cb35-15"><a href="#cb35-15" tabindex="-1"></a>                       <span class="fu">quantile</span>(treatment_effects_cabap, <span class="fl">0.975</span>))</span>
<span id="cb35-16"><a href="#cb35-16" tabindex="-1"></a></span>
<span id="cb35-17"><a href="#cb35-17" tabindex="-1"></a><span class="co">#feeling thermometer</span></span>
<span id="cb35-18"><a href="#cb35-18" tabindex="-1"></a></span>
<span id="cb35-19"><a href="#cb35-19" tabindex="-1"></a></span>
<span id="cb35-20"><a href="#cb35-20" tabindex="-1"></a>cf_ftj <span class="ot">&lt;-</span> <span class="fu">causal_forest</span>(</span>
<span id="cb35-21"><a href="#cb35-21" tabindex="-1"></a>  <span class="at">X =</span> df_rdd66[, <span class="fu">c</span>(<span class="st">&quot;age&quot;</span>,<span class="st">&quot;female&quot;</span>,<span class="st">&quot;education&quot;</span>,<span class="st">&quot;income&quot;</span>,<span class="st">&quot;psu_rul&quot;</span>)],</span>
<span id="cb35-22"><a href="#cb35-22" tabindex="-1"></a>  <span class="at">Y =</span> df_rdd66<span class="sc">$</span>ftj,</span>
<span id="cb35-23"><a href="#cb35-23" tabindex="-1"></a>  <span class="at">W =</span> df_rdd66<span class="sc">$</span>cutoff,</span>
<span id="cb35-24"><a href="#cb35-24" tabindex="-1"></a>  <span class="at">seed =</span> <span class="dv">2025</span>)</span>
<span id="cb35-25"><a href="#cb35-25" tabindex="-1"></a></span>
<span id="cb35-26"><a href="#cb35-26" tabindex="-1"></a>treatment_effects_ftj <span class="ot">&lt;-</span> <span class="fu">predict</span>(cf_ftj)<span class="sc">$</span>predictions</span>
<span id="cb35-27"><a href="#cb35-27" tabindex="-1"></a></span>
<span id="cb35-28"><a href="#cb35-28" tabindex="-1"></a>mean_effect_ftj <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="fu">mean</span>(treatment_effects_ftj), </span>
<span id="cb35-29"><a href="#cb35-29" tabindex="-1"></a>                     <span class="fu">quantile</span>(treatment_effects_ftj, <span class="fl">0.025</span>), </span>
<span id="cb35-30"><a href="#cb35-30" tabindex="-1"></a>                     <span class="fu">quantile</span>(treatment_effects_ftj, <span class="fl">0.975</span>))</span>
<span id="cb35-31"><a href="#cb35-31" tabindex="-1"></a></span>
<span id="cb35-32"><a href="#cb35-32" tabindex="-1"></a></span>
<span id="cb35-33"><a href="#cb35-33" tabindex="-1"></a>df_cf <span class="ot">&lt;-</span> <span class="fu">t</span>(<span class="fu">cbind</span>(mean_effect_cabap, mean_effect_ftj))</span>
<span id="cb35-34"><a href="#cb35-34" tabindex="-1"></a><span class="fu">colnames</span>(df_cf) <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;mean&quot;</span>, <span class="st">&quot;up&quot;</span>, <span class="st">&quot;low&quot;</span>)</span>
<span id="cb35-35"><a href="#cb35-35" tabindex="-1"></a></span>
<span id="cb35-36"><a href="#cb35-36" tabindex="-1"></a>df_cf <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(<span class="at">outcome =</span> <span class="fu">c</span>(<span class="st">&quot;mean_effect_cabap&quot;</span>, <span class="st">&quot;mean_effect_ftj&quot;</span>),df_cf)</span>
<span id="cb35-37"><a href="#cb35-37" tabindex="-1"></a></span>
<span id="cb35-38"><a href="#cb35-38" tabindex="-1"></a>df_cf<span class="sc">$</span>mean <span class="ot">&lt;-</span> <span class="fu">round</span>(df_cf<span class="sc">$</span>mean, <span class="dv">3</span>)</span>
<span id="cb35-39"><a href="#cb35-39" tabindex="-1"></a>df_cf<span class="sc">$</span>up <span class="ot">&lt;-</span> <span class="fu">round</span>(df_cf<span class="sc">$</span>up, <span class="dv">3</span>)</span>
<span id="cb35-40"><a href="#cb35-40" tabindex="-1"></a>df_cf<span class="sc">$</span>low <span class="ot">&lt;-</span> <span class="fu">round</span>(df_cf<span class="sc">$</span>low, <span class="dv">3</span>)</span>
<span id="cb35-41"><a href="#cb35-41" tabindex="-1"></a></span>
<span id="cb35-42"><a href="#cb35-42" tabindex="-1"></a>format_ci <span class="ot">&lt;-</span> <span class="cf">function</span>(mean, low, up) {</span>
<span id="cb35-43"><a href="#cb35-43" tabindex="-1"></a>  <span class="fu">sprintf</span>(<span class="st">&quot;%.3f</span><span class="sc">\n</span><span class="st">[%.3f, %.3f]&quot;</span>, mean, low, up)</span>
<span id="cb35-44"><a href="#cb35-44" tabindex="-1"></a>}</span>
<span id="cb35-45"><a href="#cb35-45" tabindex="-1"></a></span>
<span id="cb35-46"><a href="#cb35-46" tabindex="-1"></a>formatted_results <span class="ot">&lt;-</span> <span class="fu">mapply</span>(format_ci, df_cf<span class="sc">$</span>mean, df_cf<span class="sc">$</span>low, df_cf<span class="sc">$</span>up)</span>
<span id="cb35-47"><a href="#cb35-47" tabindex="-1"></a></span>
<span id="cb35-48"><a href="#cb35-48" tabindex="-1"></a>df_formatted <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb35-49"><a href="#cb35-49" tabindex="-1"></a>  <span class="at">Outcome =</span> df_cf<span class="sc">$</span>outcome,</span>
<span id="cb35-50"><a href="#cb35-50" tabindex="-1"></a>  <span class="at">Results =</span> formatted_results</span>
<span id="cb35-51"><a href="#cb35-51" tabindex="-1"></a>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;">
Outcome variables
</th>
<th style="text-align:center;">
Results
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Cabinet approval
</td>
<td style="text-align:center;">
0.056 [0.138, -0.043]
</td>
</tr>
<tr>
<td style="text-align:left;">
Feeling thermometer
</td>
<td style="text-align:center;">
-0.385 [3.017, -3.445]
</td>
</tr>
</tbody>
</table>
</div>
</div>
<div id="d.3-visualizing-rolling-rdd-results" class="section level2 unnumbered">
<h2 class="unnumbered">D.3: Visualizing Rolling RDD Results</h2>
<div id="figure-d5-and-d6-gradually-shifting-the-cutoff-before-and-after-the-first-gold-medal-does-not-alter-the-null-results-for-cabinet-approval" class="section level3 unnumbered">
<h3 class="unnumbered">Figure D5 and D6: Gradually shifting the cutoff
(Before and After the first gold medal) does not alter the null results
for cabinet approval</h3>
<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1" tabindex="-1"></a><span class="co">#cabinet approval</span></span>
<span id="cb36-2"><a href="#cb36-2" tabindex="-1"></a></span>
<span id="cb36-3"><a href="#cb36-3" tabindex="-1"></a>cutoff_time <span class="ot">&lt;-</span> <span class="fu">as.POSIXct</span>(<span class="st">&quot;2024-07-28 01:01:00&quot;</span>)</span>
<span id="cb36-4"><a href="#cb36-4" tabindex="-1"></a></span>
<span id="cb36-5"><a href="#cb36-5" tabindex="-1"></a>df_rdd48 <span class="ot">&lt;-</span> df_rdd48 <span class="sc">|&gt;</span></span>
<span id="cb36-6"><a href="#cb36-6" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">running_var =</span> <span class="fu">as.numeric</span>(<span class="fu">difftime</span>(StartDate, cutoff_time, <span class="at">units =</span> <span class="st">&quot;mins&quot;</span>)))</span>
<span id="cb36-7"><a href="#cb36-7" tabindex="-1"></a></span>
<span id="cb36-8"><a href="#cb36-8" tabindex="-1"></a>cutoff_diffs <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">20</span>, <span class="sc">-</span><span class="dv">15</span>, <span class="sc">-</span><span class="dv">5</span>, <span class="sc">-</span><span class="dv">3</span>, <span class="sc">-</span><span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">15</span>, <span class="dv">20</span>)</span>
<span id="cb36-9"><a href="#cb36-9" tabindex="-1"></a>titles <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;20 mins before&quot;</span>, <span class="st">&quot;15 mins before&quot;</span>, <span class="st">&quot;5 mins before&quot;</span>, <span class="st">&quot;3 mins before&quot;</span>, <span class="st">&quot;1 min before&quot;</span>,</span>
<span id="cb36-10"><a href="#cb36-10" tabindex="-1"></a>            <span class="st">&quot;At cutoff&quot;</span>, <span class="st">&quot;1 min after&quot;</span>, <span class="st">&quot;3 mins after&quot;</span>, <span class="st">&quot;5 mins after&quot;</span>, <span class="st">&quot;15 mins after&quot;</span>, <span class="st">&quot;20 mins after&quot;</span>)</span>
<span id="cb36-11"><a href="#cb36-11" tabindex="-1"></a></span>
<span id="cb36-12"><a href="#cb36-12" tabindex="-1"></a>plots <span class="ot">&lt;-</span> <span class="fu">vector</span>(<span class="st">&quot;list&quot;</span>, <span class="fu">length</span>(cutoff_diffs))</span>
<span id="cb36-13"><a href="#cb36-13" tabindex="-1"></a></span>
<span id="cb36-14"><a href="#cb36-14" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">seq_along</span>(cutoff_diffs)) {</span>
<span id="cb36-15"><a href="#cb36-15" tabindex="-1"></a>  cutoff_range <span class="ot">&lt;-</span> <span class="fu">range</span>(df_rdd48<span class="sc">$</span>running_var, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb36-16"><a href="#cb36-16" tabindex="-1"></a>  </span>
<span id="cb36-17"><a href="#cb36-17" tabindex="-1"></a>  <span class="fu">invisible</span>(<span class="fu">capture.output</span>({</span>
<span id="cb36-18"><a href="#cb36-18" tabindex="-1"></a>    <span class="fu">suppressMessages</span>({</span>
<span id="cb36-19"><a href="#cb36-19" tabindex="-1"></a>      rd <span class="ot">&lt;-</span> rdrobust<span class="sc">::</span><span class="fu">rdplot</span>(</span>
<span id="cb36-20"><a href="#cb36-20" tabindex="-1"></a>        <span class="at">y     =</span> df_rdd48<span class="sc">$</span>cabap,</span>
<span id="cb36-21"><a href="#cb36-21" tabindex="-1"></a>        <span class="at">x     =</span> df_rdd48<span class="sc">$</span>running_var,</span>
<span id="cb36-22"><a href="#cb36-22" tabindex="-1"></a>        <span class="at">covs  =</span> <span class="fu">cbind</span>(df_rdd48<span class="sc">$</span>age, df_rdd48<span class="sc">$</span>female, df_rdd48<span class="sc">$</span>income, df_rdd48<span class="sc">$</span>education, df_rdd48<span class="sc">$</span>psu_rul),</span>
<span id="cb36-23"><a href="#cb36-23" tabindex="-1"></a>        <span class="at">x.lim =</span> cutoff_range,</span>
<span id="cb36-24"><a href="#cb36-24" tabindex="-1"></a>        <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb36-25"><a href="#cb36-25" tabindex="-1"></a>        <span class="at">y.lab =</span> <span class="st">&quot;Cabinet approval&quot;</span>,</span>
<span id="cb36-26"><a href="#cb36-26" tabindex="-1"></a>        <span class="at">title =</span> titles[i],</span>
<span id="cb36-27"><a href="#cb36-27" tabindex="-1"></a>        <span class="at">c     =</span> cutoff_diffs[i]</span>
<span id="cb36-28"><a href="#cb36-28" tabindex="-1"></a>      )</span>
<span id="cb36-29"><a href="#cb36-29" tabindex="-1"></a>    })</span>
<span id="cb36-30"><a href="#cb36-30" tabindex="-1"></a>  }, <span class="at">type =</span> <span class="st">&quot;message&quot;</span>))  </span>
<span id="cb36-31"><a href="#cb36-31" tabindex="-1"></a>  </span>
<span id="cb36-32"><a href="#cb36-32" tabindex="-1"></a>  plots[[i]] <span class="ot">&lt;-</span> rd<span class="sc">$</span>rdplot <span class="sc">+</span> <span class="fu">theme_igray</span>()</span>
<span id="cb36-33"><a href="#cb36-33" tabindex="-1"></a>}</span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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hLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFd7cCeoeAJnCTsOhKuElYF10CGjACqsCiK+EmYV10qwT023/o92/801fLB598QEAVVJdzrhIWXQk3CeuiWyOgf+gP98xvlw5efLUKR0AzuElYdCXcJKyLboWAPrjZf/aTs4cf959dvA1KQKVUl3OuEhZdCTcJ66JbIaC3xuW81f/54sGLr1bhCGgGNwmLroSbhHXRvXxAH74zDufd/nOLBy++WoUjoBncJCy6Em4S1kW3RkBv/Hr4wYObi/fhCaiU6nLOVcKiK+EmYV10QwL6p5N9LV4TUDWTMba3q/I10OfGPxJQxtg+rc6j8D/+qnkUvv/DhScycRdeSnW511MJi66Em4R10a3xPNAvR88D/Ue+BjoB51BdzrlKWHQl3CSsi26VVyL98af9/t9/woNIU3AO1eWcq4RFV8JNwrroVnwtPE9jmoJzqC7nXCUsuhJuEtZFt2JAb/V/snjwyQcEVEF1OecqYdGVcJOwLroVAvrlKJwPbo6fzjRz8OKrVTgCmsFNwqIr4SZhXXQrBPRu/8bPzs7+eHPpBigB1VJdzrlKWHQl3CSsi26Nu/Afjx6FX3ovEQKqpbqcc5Ww6Eq4SVgX3SpfA/3D3/b7f/GzFQeffEBAFVSXc64SFl0JNwnross70geMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgX3Z0K6J07ZwRUz03CoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10YwP6tXZ37nzd64mZjLH9HbdAA8YtUAUWXQk3CeuiS0ADRkAVWHQl3CSsiy4BDRgBVWDRlXCTsC66BDRgBFSBRVfCTcK66BLQgBFQBRZdCTcJ66JLQANGQBVYdCXcJKyLLgENGAFVYNGVcJOwLroENGAEVIFFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsi+4uBXTQTwKawE3CoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQvCujpL15e3l990fLgxVeraAQ0h5uERVfCTcK66F4U0O+e7y3vBx+1PHjx1SoaAc3hJmHRlXCTsC66BDRgBFSBRVfCTcK66PI10IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsiy4BDRgBVWDRlXCTsC66HQJ6+vvxbv8lXwPdOAKqwKIr4SZhXXTbBvT+mzyI1HoEVIFFV8JNwrrotgzo/IPxjxLQjSOgCiy6Em4S1kW3ZUCPe72DJ146bP7XO3i19cGLr1bRCGgONwmLroSbhHXRbRfQ0zd6j37R/P9rTUsfbflCJAKqpbqcc5Ww6Eq4SVgX3XYB/e75g7fPmnY+Pfj/oyaj7Q5efLWKRkBzuElYdCXcJKyLbtuADh83Ouldm/5/q4MXX62iEdAcbhIWXQk3Ceui2zGgzb33755vex+egEqpLudcJSy6Em4S1kW37ddAh3fh7x02HR3XtM3Bi69W0QhoDjcJi66Em4R10W35KPzR8Kufoy+FjjLa6uDFV6toBDSHm4RFV8JNwrrotgzovcPekx82D8M/3cSUu/CbR0AVWHQl3CSsi27bVyIdDV9/dNLrHRz2hrdGWx28+GoVjYDmcJOw6Eq4SVgX3davhf9dc8f99Gj4QiSeB7p5BFSBRVfCTcK66HZ4M5H/HHTz9NPr119p208CqqW6nHOVsOhKuElYF13ezi5gBFSBRVfCTcK66BLQgBFQBRZdCTcJ66Lb9on0T35YcG0IqJTqcs5VwqIr4SZhXXTbBrTXO2j/tc/pwScfEFAF1eWcq4RFV8JNwrrotnwl0ruHzcPvj7/V8eDFV6toBDSHm4RFV8JNwrrotv4a6O0Xm4QedLorT0ClVJdzrhIWXQk3Ceui2+V7In36WNPQR17laUwXjIAqsOhKuElYF91uj8J/8/7wrvyf8VLOjSOgCiy6Em4S1kW389OYmu8ux5uJbB4BVWDRlXCTsC66HQN6+v5jfFfOC0dAFVh0JdwkrItul4Ce3n6h2xdBCaiU6nLOVcKiK+EmYV102wf0s+4PwxNQKdXlnKuERVfCTcK66LYM6P03h48eXe32RFACKqW6nHOVsOhKuElYF932r0QqeCkSAZVSXc65Slh0JdwkrItu64A++Zvu14aASqku51wlLLoSbhLWRbflSzn/o/Pr4IcHn3xAQBVUl3OuEhZdCTcJ66LL29kFjIAqsOhKuElYF932Af3m/eu93sH1Vz7vcPDiq1U0AprDTcKiK+EmYV10Wwf0uDdZ228pR0DFVJdzrhIWXQk3Ceui2zagTT8feeLllx7rUlACKqW6nHOVsOhKuElYF9323xf+0dEz6O+/0Tt4u+3Bi69W0QhoDjcJi66Em4R10W0Z0KPz72V8+kbvWtuDF1+tohHQHG4SFl0JNwnrotvyaUxvzNzqvHfY9hvDawPa9JOAJnCTsOhKuElYF922T6SfeQOmuZ9sPnjx1SoZAU3B8immwaIr4Xa9AAENGAFVYNGVcJOwLrpt78L3Xpv+5KTHXfjNI6AKLLoSbhLWRXeHHkQioClYPsU0WHQl3K4XaP80pqujdxP57IVtfRoTAU3B8immwaIr4Xa9QJcn0veuX7/e6aVIBFRKdTnnKmHRlXCTsC66rV/K+enh+JWcB6+2P3jx1SoZAU3B8immwaIr4Xa9QPs3Ezm9/dLgFugTb3V4YzsCKqW6nHOVsOhKuElYF90dejs7ApqC5VNMg0VXwu16gbaPwnf5XnLnB598QEAVVJdzrhIWXQk3Ceui2/pbery28jcuOPjkAwKqoLqcc5Ww6Eq4SVgX3YJXInU4+OQDAqqgupxzlbDoSrhJWBfdgjcT6XDwyQcEVEF1OecqYdGVcJOwLrotvwZ6PHk70G4Hn3xAQBVUl3OuEhZdCTcJ66LbMqDfvDd8Q/rx/orXwm8cAVVg0ZVwk7Auuq0fRJod78a0eQRUgUVXwk3CuugS0IARUAUWXQk3CeuiyxPpA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7Auut0C+vuOB598QEAVVJdzrhIWXQk3Ceui2z6gt18cvhdTl9d0ElAp1eWcq4RFV8JNwrrotg3o6RvTh5Ceav1+TARUSnU55yph0ZVwk7Auui0D2vTz4Il/+eDfXjrstf6OHgRUS3U55yph0ZVwk7Auuq1fidT789ENz9P3eq3fWISASqku51wlLLoSbhLWRbf1d+U8/z4eR3xTuQtGQBVYdCXcJKyLbtsn0s+8mci9Q55Iv3kEVIFFV8JNwrroFryd3fJ72z38+Ga//xc/Wz548dUqGQFNwfIppsGiK+F2vUDB29ndO3x0/nH4b1/vD/fjpYMXX62SEdAULJ9iGiy6Em7XC7R+EOnayo+bPXyn/8wnZw//b//GrxcPXny1SkZAU7B8immw6Eq4XS/Q/mlMk6d/Hi++l8jd/g9/2/z4Zf+5xYMXX62SEdAULJ9iGiy6Em7XC7S8C/+L5vmfT7zywb83Pz4+966ggxugP1938OKrVTICmoLlU0yDRVfC7XqBorezm3tTu+9fH90AXXXw4qtVMgKaguVTTINFV8LteoHLB/TBzWe/+sPf9vvP/GrmoON9rdydO4P/6/WkTMbYPu/yb2c3COgvR4/C/4SAMsb2aZcP6N3mCUxfnT38mEfhp+Acqsu9nkpYdCXcJKyL7uXfD/Tu5KbnLR6Fn4BzqC7nXCUsuhJuEtZF9/LvB/rg5viWZ/PF0IWDF1+tkhHQFCyfYhosuhJu1wtc/v1AH9wcPwo//eD84MVXq2QENAXLp5gGi66E2/UCl38/0IfvjG+B3u1zC3QMzqG6nHOVsOhKuElYF90K7wc6+drnrfOH4ScHL75aJSOgKVg+xTRYdCXcrheo8H6gD272f/QJj8LPjIAqsOhKuElYF90a7wd69+bwaaA3ll7RSUClVJdzrhIWXQk3CeuiW+X9QL/95SChf//J8sGLr1bJCGgKlk8xDRZdCbfrBWq8H+j6gxdfrZIR0BQsn2IaLLoSbtcLVHg/0A0HL75aJSOgKVg+xTRYdCXcrheo8H6gGw5efLVKRkBTsHyKabDoSrhdL3D59wPddPDiq1UyApqC5VNMg0VXwu16gcu/nd2mgxdfrZIR0BQsn2IaLLoSbtcLENCAEVAFFl0JNwnronv5t7PbdPDJBwRUQXU55yph0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3Ceui2yGgp78f7/ZftnwekzSgw34S0ARuEhZdCTcJ66LbNqD33+z08NH44MVXq2AE1OWcq4RFV8JNwrrotgzo/MPwjxLQjSOgCiy6Em4S1kW3/fuBHjzx0mHzv97Bq0uB7jgAACAASURBVK0PXny1CkZAXc65Slh0JdwkrItu6/cDffSL5v9fa1ra9r1ECKiW6nLOVcKiK+EmYV10O70f6PHwbZWPFt6RfsPBi69WwQioyzlXCYuuhJuEddHt9H6gJ8P3YTrZzndjIqAu51wlLLoSbhLWRbdjQJt77989v5XvB0pAXc65Slh0JdwkrItupzdUHn0zj+V3pF978OKrVTAC6nLOVcKiK+EmYV10Wz4KfzT86ufoS6FL3xNp/cGLr1bBCKjLOVcJi66Em4R10W0Z0HuHvSc/HH9zzqPWD8MTUCnV5ZyrhEVXwk3Cuui2fSXS0fD1Rye93sFhb+ZbHF9w8OKrVTAC6nLOVcKiK+EmYV10W78W/nfNHffTo+ELkXge6OYRUAUWXQk3Ceui2+HNRP5z0M3TT69ff6VtPwmolupyzlXCoivhJmFddHfn7ewIqMs5VwmLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10W35dna/+Kvzlx/de+nPeDORjSOgCiy6Em4S1kW30xsqr/jJ5oMXX62CEVCXc64SFl0JNwnrolsQUN4P9KIRUAUWXQk3Ceuie2FA578j/Pj7wnMXfuMIqAKLroSbhHXRvfgW6MlyQPmunJtHQBVYdCXcJKyL7sUBPf0/L7/80uHBEy9P9te/aX3w4qtVMALqcs5VwqIr4SZhXXQLvgba4eCTDwiogupyzlXCoivhJmFddAuextTh4JMPCKiC6nLOVcKiK+EmYV10Oz2R/vTzjgeffEBAFVSXc64SFl0JNwnrots+oLdfbL6v3Hf/tf139CCgWqrLOVcJi66Em4R10W0b0NN3m4ffBwF9vne19ZdDCaiU6nLOVcKiK+EmYV10O3xb46v/7fAHH53+D74r54UjoAosuhJuEtZFt2VAT3q9V8ePxX96yPNALxgBVWDRlXCTsC66LQN61Ht6+mSm4961tgcvvloFI6Au51wlLLoSbhLWRbfl05jeOHh7GlBeC3/RCKgCi66Em4R10e3yRPpxQHk3potGQBVYdCXcJKyLLgENGAFVYNGVcJOwLrpt78I3DxyNy3nCuzFdMAKqwKIr4SZhXXRbPog0fOBoFNBBTHkQafMIqAKLroSbhHXRbRnQe4e9p74YBvT+C73mAaV2By++WgUjoC7nXCUsuhJuEtZFt+0T6Y97vd71w4MnHhv8+HTrgxdfre4b9ZOAJnCTsOhKuElYF93Wr4X/3eHk7ZRb95OAaqku51wlLLoSbhLWRbf9m4l88/71QT0fefLDDgcvvlrdR0BtzrlKWHQl3CSsi+7OfF94AmpzzlXCoivhJmFddAlowAioAouuhJuEddHtEtDfT9b2fZUJqJTqcs5VwqIr4SZhXXTbBvT+mzPflXMbX4lEQG3OuUpYdCXcJKyLbsuAzn93eAK6eQRUgUVXwk3Cuui2fiVS7+pffzDZf2zhSzkJqM05VwmLroSbhHXRbf1a+LYv35w7+OQDAqqgupxzlbDoSrhJWBfdtu/G1Prlm3MHn3xAQBVUl3OuEhZdCTcJ66Lb5e3sOo+ASqku51wlLLoSbhLWRbfLO9J3HgGVUl3OuUpYdCXcJKyLbusHkdq/An7m4JMPCKiC6nLOVcKiK+EmYV10WwZ0cBP01e7XhoBKqS7nXCUsuhJuEtZFt+Vd+F+81OsdPPHyeH/F05g2joAqsOhKuElYF922DyL1eCJ9+xFQBRZdCTcJ66JLQANGQBVYdCXcJKyLLu/GFDACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kX3ooCe/qJ5zH3w/7PjUfjNI6AKLLoSbhLWRfeigH73fPOQEQ8idRoBVWDRlXCTsC66BDRgBFSBRVfCTcK66PI10IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsi26ngJ7+vuPBJx8QUAXV5ZyrhEVXwk3Cuui2D+jtF4df/3zyww4HL75a3UdAbc65Slh0JdwkrItu24CevjF9COmplk9iIqBiqss5VwmLroSbhHXRbf12ds27Mf3zB//20qCg11ofvPhqdR8BtTnnKmHRlXCTsC66LQN60pu8o/Lpe73ea20PXny1uo+A2pxzlbDoSrhJWBfd1t+V8/wd6Y9a3wQloFKqyzlXCYuuhJuEddEt+K6c9w55Iv3mEVAFFl0JNwnrolvwXTnbf4tOAiqlupxzlbDoSrhJWBfdgu/Kee/wUd5MZOMIqAKLroSbhHXRbf1dOa/NfNz2O3QSUCnV5ZyrhEVXwk3Cuui2DOh3z/f+fHyz87j1e4kQUC3V5ZyrhEVXwk3Cuui2eD/Q4V4aPQ/0318+7PWe4P1AN4+AKrDoSrhJWBfdFm9nt7zWDyJ9rdudO8Mfej0hkzG234sN6OSD+P+c3OEWqM1/tCth0ZVwk7AuurvydnbjfhLQBG4SFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7Auuh0Cevr78W7/5TZ+DXT0IwHVc5Ow6Eq4SVgX3bYBvf/mtj+INPqRgOq5SVh0JdwkrItu+yfSz+xRArpxBFSBRVfCTcK66LZ+KWfv4ImXDpv/9Q5ebX3w4qvVeQTU55yrhEVXwk3Cuui2fj/QR79o/v+1pqVt30uEgGqpLudcJSy6Em4S1kW30/uBjt5G5Ggb35GegPqcc5Ww6Eq4SVgX3U7vB3oyfE+mk218R3oC6nPOVcKiK+EmYV10Owa0uff+3fNb+H6gBNTnnKuERVfCTcK66HZ6Q+XRN/PYynekJ6A+51wlLLoSbhLWRbflo/BHw69+jr4UupXfE4mA+pxzlbDoSrhJWBfdlgG9d9h78sPxN+c8av0wPAGVUl3OuUpYdCXcJKyLbttXIh0NX3900usdHPa28Vt6EFCfc64SFl0JNwnrotv6tfC/a+64nx4NX4jE80A3j4AqsOhKuElYF90Obybyn4Nunn56/forbftJQLVUl3OuEhZdCTcJ66LL29kFjIAqsOhKuElYF10CGjACqsCiK+EmYV102wX0m89HP957/K3W99/PCKiY6nLOVcKiK+EmYV102wT0/guTx92bN2V6tcPBi69W5xFQn3OuEhZdCTcJ66LbIqCfHvYmr35/r3kQvu2TmAiomOpyzlXCoivhJmFddC8O6MmgmVd/M/7J6buDn7V9MyYCqqW6nHOVsOhKuElYF90LA9q8F/3sbc7j9t/Rg4BqqS7nXCUsuhJuEtZF98KAHi88cf70DV6JdNEIqAKLroSbhHXRvSigTS/n77KftH8pEgGVUl3OuUpYdCXcJKyL7kUBHdyDH76T3fnuHfJdOS8YAVVg0ZVwk7Auui0CupDL5V9Zf/Diq9V5BNTnnKuERVfCTcK66BLQgBFQBRZdCTcJ66Lb4mugy3fh+Rro5hFQBRZdCTcJ66J74aPwR4sPuh/3+KZyF4yAKrDoSrhJWBfdCwO6+KA7T2O6eARUgUVXwk3Cuuh2fiL9EU+kv3AEVIFFV8JNwrrotnsp51OT26D33+zyYngCKqW6nHOVsOhKuElYF90WbyZy3LyDyNV//uCDD95/ofmw7VdACaiY6nLOVcKiK+EmYV1027yd3e8Oe+c7+JsOBy++Wp1HQH3OuUpYdCXcJKyLbqs3VB7ecR/l86nPuxy8+Gp1HgH1OecqYdGVcJOwLrptv6XHZ//+i5f/+oMu9TwjoGKqyzlXCYuuhJuEddHleyIFjIAqsOhKuElYF90dCegdAupzzlXCoivhJmFddHcmoOMPCKiem4RFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsiy4BDRgBVWDRlXCTsC66BDRgBFSBRVfCTcK66BLQgBFQBRZdCTcJ66JLQANGQBVYdCXcJKyLLgENGAFVYNGVcJOwLroENGAEVIFFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsiy4BDRgBVWDRlXCTsC66BDRgBFSBRVfCTcK66BLQgBFQBRZdCTcJ66JLQANGQBVYdCXcJKyLLgENGAFVYNGVcJOwLroENGAEVIFFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsi+5uBPQOATU65yph0ZVwk7AuursS0MlHBFTPTcKiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRbdKQP/4037/xj99tXzwyQcEVEF1OecqYdGVcJOwLro1Avplf7hnfrt08OKr1XEE9MzonKuERVfCTcK66FYI6IObN352dvbtT/vPLR28+Gp1HAE9MzrnKmHRlXCTsC66FQJ6q/+T5ocHN3+4eBOUgEqpLudcJSy6Em4S1kW33oNI379OQCfgHKrLOVcJi66Em4R10a0X0Ac3n118GImASqku51wlLLoSbhLWRbdaQP9ws//z84OO97Vod+5MPur1VEzG2N6vUkBv9fs3fjX9GQFljO3D6gT04b/+3c3+jf+++MvchZdSXe71VMKiK+EmYV10630N9I8z9+EnB598EPy3cYeAnhmdc5Ww6Eq4SVgX3Yov5bzbX3wUSRfQ6YcEVM9NwqIr4SZhXXQrBnT5YXgCKqW6nHOVsOhKuElYF93LB/ThO+O77gR0Cs6hupxzlbDoSrhJWBfdKq9Eem7ux5mDF1+tbiOgQ6rLOVcJi66Em4R10a3yWvj+j786e/hx/8avFw9efLW6jYAOqS7nXCUsuhJuEtZFt8bXQO+O3o3pxuKD8ARUS3U55yph0ZVwk7AuulUeRPr2Hwb5/NEnyweffEBAFVSXc64SFl0JNwnrorsT70hPQIdUl3OuEhZdCTcJ66JLQANGQBVYdCXcJKyLLgENGAFVYNGVcJOwLroENGAEVIFFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsiy4BDRgBVWDRlXCTsC66BDRgBFSBRVfCTcK66BLQgBFQBRZdCTcJ66JLQANGQBVYdCXcJKyLLgENGAFVYNGVcJOwLroENGAEVIFFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiuwsBvUNAh1SXc64SFl0JNwnrorsbAT3/mIDquUlYdCXcJKyLLgENGAFVYNGVcJOwLroENGAEVIFFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsiy4BDRgBVWDRlXCTsC66BDRgBFSBRVfCTcK66BLQgBFQBRZdCTcJ66JLQANGQBVYdCXcJKyLLgENGAFVYNGVcJOwLroENGAEVIFFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsiy4BDRgBVWDRlXCTsC66BDRgBFSBRVfCTcK66O5AQO8Q0BHV5ZyrhEVXwk3CuujuREBnfkJA9dwkLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFdwcCekZAR1SXc64SFl0JNwnrorsDAeUW6Jjqcs5VwqIr4SZhXXT9AzrXTwKawE3CoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddEloAEjoAosuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10fUP6BkBHVNdzrlKWHQl3CSsi65/QLkFOqG6nHOVsOhKuElYF137gM73k4AmcJOw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBfd2IB+Hb87d+Z+2usJmIwx1oxboAHjFqgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddO0DekZAJ1SXc64SFl0JNwnromsfUG6BTqku51wlLLoSbhLWRdc9oAv9JKAJ3CQsuhJuEtZFl4AGjIAqsOhKuElYF10CGjACqsCiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddF1D+gZAZ1SXc65Slh0JdwkrIuue0C5BXpOdTnnKmHRlXCTsC665gFd7CcBTeAmYdGVcJOwLroENGAEVIFFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsiy4BDRgBVWDRlXCTsC66BDRgBFSBRVfCTcK66BLQgBFQBRZdCTcJ66JLQANGQBVYdCXcJKyLrnlAF9+MiYAmcJOw6Eq4SVgXXfOAcgt0hupyzlXCoivhJmFddL0DutRPAprATcKiK+EmYV10CWjACKgCi66Em4R10SWgASOgCiy6Em4S1kWXgAaMgCqw6Eq4SVgXXQIaMAKqwKIr4SZhXXQJaMAIqAKLroSbhHXRJaABI6AKLLoSbhLWRZeABoyAKrDoSrhJWBddAhowAqrAoivhJmFddAlowAioAouuhJuEddG1DugdAjpLdTnnKmHRlXCTsC665gFd+iUCqucmYdGVcJOwLroENGAEVIGty70yWDtuVWzr8a+r4Xa9AAENGAFVYKtyr1xpW9Cd0O3ATcK66BLQgBFQBbYm98qV1gXdBd0u3CSsiy4BDRgBVWArcq9caV/QHdDtxE3CuugS0IBtS0Bbf13vctx4xEosAZVwk7AuugQ0YFsS0PZf17scN5ywGktAJdwkrIsuAQ3YdgS0w9f1LseNBqzBElAJNwnrousc0BXPoyeg03WJwuW4wcdfh+VBJAk3Ceui6x3Q5V8joJMR0C7jaUzruElYF10CGjACKhhPpNdwk7AuugQ0YARUMJtPsUpYdCXcrhfYyoC2+awf/BECOk+t+iBS+0vyKSbBoivhdr3ANga0zef9FQK6RK35NKYOl+VTTIJFV8LteoEtDGibW05XRgFd/jMEdGaX7SePqqziJmHRlXC7XmD7Atrma3dXJgFd+jMEtMY6ff2UTzEJFl0Jt+sFjAN6h4DOUwmohJuERVfC7XoB64Cu+DMEtMYI6AZuEhZdCbfrBQhowAioYDafYpWw6Eq4XS+wfQFt/SDSqnvwBLTOeBBpPTcJi66E2/UCWxjQtk9jWvUgPAGtNJ7GtJabhEVXwu16gW0MaMsn0q96GigBrbX2T4Hank+x8qdtdeGGE1ZjXYpSCeuiu5UBbbVV78VEQDO4SdiVT3vl3ftqc5OwLrrOAV31q1sZ0Mt8Xne4JehyzlXCrn7hldtL/1t/pXnP/3VV3K4X8AjoqrPMJ6CX+bzu8rVIl3OuEnbl33J8Qevqtn+uw37/68q4XS9gEdCVZ5lNQC/zed3p0XCXc64SdhcC2v4q7/m/rozb9QJbH9Ar6z4xXAJ6mU/sbs/HdDnnKmF3IKAdrvOe/+sOt41foNn2gF6Z3exvrH4MiYDqtyWfYgQ0ZFvyr9tsK/91tzygc/1cCOjKCxBQ+bblU0zTTwKqwa5+klr4v+9uBHQax/l+Xjqggk+vEXgRurMBvbNTa6VMQCXYNU9Si/4U3oOAjj8oCKjkBsoQvAK7tQ8iZVdrN1b0V8+DSOuxsoDO/wPuSkBHf0/nf2ejX2n+6qa/0DmgkrsAI/D8Ty+D7XDZgW5KOLbmU+yyU/6lTbbV9y+G3CRsZEA3/JvsRkDHBT2v53DDv7vzX1t9yfUB1dwFGIEXfl4KlX86V7G93Lr89yJlKx/diP+nqa3b+n5NXWzb1Q1o+/N/NwJ6ZS6g56pzv7j677HXW/fXkxXQ+p9WbU8G0QKeGNkKuzUB7XpWbcM/fPtbvl2PXGeXeBDpUn9bOxHQK7MBbXynH83+6uq/yaWAWq/TX9v2FKV8Hf4btzW6cf9djjup2l/n7QnoYvRDPpF2JKDnBW3sZ292zn606rKLd+FjzsFOK/zL67ytKUr5utxL2BZd0T2btTfJhh/u0Hmce0V3LaB3lr4aOvPjqstu44NIom1LUS4xAroeW+uW7+UDtS2r9Dc7t10J6Kigzd/SldnH3sflvDN5mH55YU9j6nJRAlo4AroeW5Pb4bJTrLaL2/Kve+G2MaDjgt65MxPK2Ruhd86f1LS0qCfSdzpVCWjhCOh6bHZAtduWf90Lt5UBnRZ05t94rqDr//WDXsrZ7VwloKXr8Pe8NbqSftYNqOeDSBJu1wtsZ0BXvQfTlRVbvmBMQDuerAS0eO3/lrdHV9HPygF1fBqThtv1Alsa0LPpCbL0CwR0HdXlnNu49n/J26Mr6Ge9B5FmLt2KW3b4y26L/nU3b3sDuvxPPDlfCOhqqss5Vwm797qaW77Bx1+HdfnX3eKArh0BXU11OecqYdGV3PKNBqzBuvzrVgnow1/e7Pd/9MnywScfBLyOd83Jw4NIem4SFl0JNwnrolsjoN+/3m/2w98uHbz4al2w9TWLekPlTneXCKgCi66Em4R10a0R0Fv9Zz85+/ad/rNfLR68+GpddPdk7e+GvSN9l7tL+x5QxV3LLdLVYLdI9zL/vm0f/I+4zdWG2/W4FQL64Obwtuf3r9/49eLBi69W8RfIt+9beuio2/IpJnlwY3t0Rdjt0b3sg/8ZL5Noze164AoB/bL/3PjHnywevPhqFT9Fg4DquQs/v9zTa9pjt0RXhd0a3cv8+7a+bGXd9tyuR64Q0Fv9nw9/vDsO6czBS69Wx0e9Z0ZA9dz5n5b/23XEboeuDLstupf5921/2bq6HbhdD335gD58Z3zX/cHNyRdB/3Syrwt3Ltz1kr1eKZPVWfm/HXPYZf59s86NQC4BZXVHQHd7BHRuVQO6+EQm7sJLqdtxJ4+78DHYbdHlLvzcQm6BTg9efLWK/40IqJ678HNNP7dGV4XdGt3L/Pu2vuwePYgUElCexlRA3ZZPMUk/t0dXhN0e3cv8+7a97D49jSngUfiz4ifrElA9d+lXFP3cIl0Ndot0L/Pv2/Ky+/RE+snzP2s+D7R4BFTPTcKiK+EmYV10t/WVSKUjoHpuEhZdCTcJ66JbIaAP3+k/U/+18IUjoHpuEhZdCTcJ66Jb481EvpW/G9P6EVA9NwmLroSbhHXRrfJ+oN/+ctDPHy3e/iSgYqrLOVcJi66Em4R10XV8R/pNI6B6bhIWXQk3CeuiS0ADRkAVWHQl3CSsiy4BDRgBVWDRlXCTsC66BDRgBFSBRVfCTcK66BLQgBFQBRZdCTcJ66JLQANGQBVYdCXcJKyLLgENGAFVYNGVcJOwLroENGAEVIFFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiS0ADRkAVWHQl3CSsiy4BDRgBVWDRlXCTsC66BDRgBFSBRVfCTcK66BLQgBFQBRZdCTcJ66JLQANGQBVYdCXcJKyLLgENJPwQbAAAColJREFUGAFVYNGVcJOwLroENGAEVIFFV8JNwrroEtCAEVAFFl0JNwnroktAA0ZAFVh0JdwkrIsuAQ0YAVVg0ZVwk7AuugQ0YARUgUVXwk3CuugS0IARUAUWXQk3CeuiGxtQxhjb6RFQxhgrXGBAk7fotuNDd5eHrscIqO3Q3eWh6zECajt0d3noeoyA2g7dXR66HiOgtkN3l4euxwio7dDd5aHrMQJqO3R3eeh6bJcCyhhj0hFQxhgrHAFljLHCEVDGGCscAWWMscIRUMYYKxwBZYyxwhFQxhgr3M4E9OEvb/b7P/ok+2rE7vvXnxt/NKe7k+5//Id+/8ZKw93U/elA95++Gv1k93WbPbj57MjXWXdXAvr96/1mP/xt9hUJ3a3+OKBzujvp/vHQqX/j181Pdl/3y5HuM8uGO6nb7OE7/VFArXV3JaC3+s9+cvbt5J9kN/fwVn8S0DndXXS/27/xs7NGaviZtPO6D24OdX86+vfded3hBv/NGDlZ6+5IQB/cHH6iff/66BbLTq65kzcO6JzuLroPbp38vPlxcHvk53ugO8jGT5ofRm67r9vswc1xQL11dySgX47L8uXoRNzFDf6D/eM/TDVndHfR/fvXx/fhbi0Z7qLuZCPtvdAd/CfyH0dfA/XW3ZGA3hrdYBnc83vugj9puy+f+dVUb053p92HAd0b3dHDKnuhe6v/3PhBJG/d3Qjow3fGt/inD+zt5san1ZzuTrsP78rtje4fbjb52Avdu4O77yMlc10C6rS9C+jwHt2e6N7q92/86mw/dIf/YSSgW7OZv3eb5z+UbDmgP/ztLrvfHT6NaT90H/7r393s3/jv+6E7/MrMUkAddXcuoCb/4Srbnt0CvXvzRvMVsX3RPTv7Y3Mffg90vxw+/s4t0K2Z39972fYroF+On0a/J7rNmi8N7r7ug5tDKQK6PbN78K5se/Uo/Mf9yXMB90J3uGE3dl53/LKr8SuOvHV3JKCTp43ZPH2sbHcXniU3furcLro/vDV+XePZHuhOXjcwCujO684H1Ft3RwJq9wKGsk0C6v3ijVa7NfNivt3XvTW9a/HcPuiONr6b7q27IwEd/Cf8GauX0JZtEtA53Z10/3LWZvd1H9zs//irs4cfj591sOu6o40D6q27IwE9+9bsTVzKNv3K0JzuDrqP35OnP371/67rNv+yozefGt6T333d4SYPFFnr7kpAz7795eCv/Ucm/9kq3fmX1ud0d8/97vlXyYbGO6472Lezb3+6+7rNpo+0O+vuTEAZY0w9AsoYY4UjoIwxVjgCyhhjhSOgjDFWOALKGGOFI6CMMVY4AsoYY4UjoIwxVjgCyhhjhSOgjDFWOALKGGOFI6CMMVY4Arr7O+rN7+nhrz36xaUOetz7wUczxzl9/7DX+8Hb0x/DJwOtJa+4BvcOr226ZKu/9NM3hn+xzGMEdPenCOiQMfiVyY/hiwd9+mer/4bWq373/OYr1O4v/d7hJf9pmHAEdPcnCOi9w96fzP4YvnjQur+hDapHw7/a7odc+mObD8O2aAR0X3b6Rm/j/cuOO5678XXS6702+2P44kHrardetdZNx3uH3Im3GQHdlwUH9ODt2R/DFw9aH9A1qoO/4EpNP6r6L8UiR0D3ZQS02zoH9KTal2TvHYr+FtmlR0D3ZfMBHeVh+Gu3X+j1Dp4a/Oz2i71e78kPx3/i/puHg1+f/nS65k8N/vjs10CPR19bPXhp/OPbixdvbpw1mEeWfue753tPn77/2OC3XvliFvz4W2ezP124HhPg2+sPvElt7tBjUnNFJgcfmB2ff7l47rirVKd/pdcuII/+0peVh4AZ5cER+CqoyQjovmxNQP9k/BDTDz56d64Jx5OHnJ5aPMroj7+5OaDzFx9c6uXJY9bzvzOoyX95YfTzqx/NgcfclddjNqBLBz74mzFyndrCUaZXZE1A5467PqD3Dsf34NeTpwGdV/50onj1o+k144F4kxHQfdmagA768cXZ6eDz/JHek5+fnb7XG33uHo9uNn3z7kJBj6Z/fu5R+MX7tQsXbziD37j/1tLvDGoyKNMXZ/ffGN/cG/z+ox8OAU+vONB0Y9D8ga/+5uzssxdGD+6sV5tsnrQQ0PO78AvHXXsXfnoPfj15GtA55UF5B9dj+KeuTY/FfXiTEdB92bqADrPRfFJfG/9G04HBJ/X4Dx/PfS4Pfv3p8S9vCujixRvOa5MDzP1OA35tfPXGdRkecPDTTddjNqCjA08uOLnkWrXJFkjrArp43LUBnX7NdD35PKCzytOvJp9fv/Mrw7Z8BHRftiag00/e6V330T3wyefy4sXGvz74FN8Q0MWLn3MWf2fap/Ghp08OOtl4PWYCOj3wpGejyK9VOz/CHGldQBePuy6g59dvPflo7r8RE+Xj5ZublR/wY3EjoPuyNQGd+1w+G3dh9s/OPho98+tHGwK6dPEpZ+l3BjWZ3sQc1WT2VuK66zEb0EWB8SHXqU03T1ob0MXjrgvo+eXXk6cBnVM+aR6h+s3Z7M4PwbZ8BHRftvZR+PHPZz/Xm3uZ5zsPzfnn/kJm5quydPEpZ+l3Frs1n8l112M2oNOHvicXHH24Tm3hb2AWtSqgS8dtFdA15PNH4edIoweaHnnl87VXjm3tCOi+rFJAJ1+b276ALt3orRTQhdvA9QM6eqpT8yj89IlMBNRlBHRf1jGgK78G1/4W6PzFZwM6/zsXBnTN1wJ36RZos89+8ViT0MlLmQioywjovqxLQNd9Da7910DnL76qc6MtB/T8yUDXNnwtcDGgq74GelFAZ0kzV2TerOhroN0DenY2+zwrvgZqMwK6L+sS0PPHjxc+l49nqrPhUfjFi89y5n9nsSYnM893em399VgO6PH05ttJb/wo/OaALpCmN3YXzBaP2+pR+C4BXf6PEo/CG42A7ss6BbR5cvcX45/PvkXG9HmZFz4PdO7i55zF31kM6PQ5PtPno668HssBXfE80M0BXSBNA30yb1b0PNBOt0CnT2M6/28EzwO1GQHdl3UK6PD1N2+drXz5zpMftnol0uzFZ9gLv7N0f/Zk6ZVIK6/HUkBnXzH09FmLgK4gNT8dvs38R2ejrp1+vnTcDa9EOu9gp4AOfn7w6uDX778xc2OXVyKZjIDuy7oF9Pw16AtfjBu/uPvq/9wY0IWLz7Lnf2f5C4LrXgs/fz2WA7r8WvgLArpAmrzK/9H3piUfvZBo/rhrAzowmb4WvtvXQO8dThzPn+LAl0BNRkD3ZR0Denb/zeZx4fO3CJrss+V3Y1pVlbmLz7HnfmfFIypL78a06nqsCOj4XZOe+nwBuTagC6TROyS9Or0inw5+89oXi8dd/3Z2c+/GtJK87kGk0/evN08EnRCaI3AP3mQElLEqq/l+oLWOxKJHQBmrsnq3G/mmSD4joIzVWa0bjtwANRoBZazSKt1y5Aao0QgoY5V20feFbze+L7zTCChjtXbv8PIvIDp9gzvwRiOgjDFWOALKGGOFI6CMMVY4AsoYY4UjoIwxVjgCyhhjhSOgjDFWOALKGGOFI6CMMVY4AsoYY4UjoIwxVjgCyhhjhSOgjDFWOALKGGOF+//GO4zXLM8VuAAAAABJRU5ErkJggg==" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" 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<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" 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<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<div class="sourceCode" id="cb48"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb48-1"><a href="#cb48-1" tabindex="-1"></a>ordered_plots1 <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb48-2"><a href="#cb48-2" tabindex="-1"></a>  plots[[<span class="dv">1</span>]], plots[[<span class="dv">2</span>]], </span>
<span id="cb48-3"><a href="#cb48-3" tabindex="-1"></a>  plots[[<span class="dv">3</span>]], plots[[<span class="dv">4</span>]], </span>
<span id="cb48-4"><a href="#cb48-4" tabindex="-1"></a>  plots[[<span class="dv">5</span>]], plots[[<span class="dv">6</span>]]</span>
<span id="cb48-5"><a href="#cb48-5" tabindex="-1"></a>)</span>
<span id="cb48-6"><a href="#cb48-6" tabindex="-1"></a></span>
<span id="cb48-7"><a href="#cb48-7" tabindex="-1"></a>ordered_plots2 <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb48-8"><a href="#cb48-8" tabindex="-1"></a>  plots[[<span class="dv">6</span>]], plots[[<span class="dv">7</span>]],  </span>
<span id="cb48-9"><a href="#cb48-9" tabindex="-1"></a>  plots[[<span class="dv">8</span>]], plots[[<span class="dv">9</span>]],</span>
<span id="cb48-10"><a href="#cb48-10" tabindex="-1"></a>  plots[[<span class="dv">10</span>]], plots[[<span class="dv">11</span>]]  </span>
<span id="cb48-11"><a href="#cb48-11" tabindex="-1"></a>)</span>
<span id="cb48-12"><a href="#cb48-12" tabindex="-1"></a></span>
<span id="cb48-13"><a href="#cb48-13" tabindex="-1"></a></span>
<span id="cb48-14"><a href="#cb48-14" tabindex="-1"></a></span>
<span id="cb48-15"><a href="#cb48-15" tabindex="-1"></a></span>
<span id="cb48-16"><a href="#cb48-16" tabindex="-1"></a><span class="co">#feeling thermometer</span></span>
<span id="cb48-17"><a href="#cb48-17" tabindex="-1"></a></span>
<span id="cb48-18"><a href="#cb48-18" tabindex="-1"></a>plots <span class="ot">&lt;-</span> <span class="fu">vector</span>(<span class="st">&quot;list&quot;</span>, <span class="fu">length</span>(cutoff_diffs))</span>
<span id="cb48-19"><a href="#cb48-19" tabindex="-1"></a></span>
<span id="cb48-20"><a href="#cb48-20" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">seq_along</span>(cutoff_diffs)) {</span>
<span id="cb48-21"><a href="#cb48-21" tabindex="-1"></a>  cutoff_range <span class="ot">&lt;-</span> <span class="fu">range</span>(df_rdd48<span class="sc">$</span>running_var, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb48-22"><a href="#cb48-22" tabindex="-1"></a>  </span>
<span id="cb48-23"><a href="#cb48-23" tabindex="-1"></a>  <span class="fu">invisible</span>(<span class="fu">capture.output</span>({</span>
<span id="cb48-24"><a href="#cb48-24" tabindex="-1"></a>    <span class="fu">suppressMessages</span>({</span>
<span id="cb48-25"><a href="#cb48-25" tabindex="-1"></a>      rd <span class="ot">&lt;-</span> rdrobust<span class="sc">::</span><span class="fu">rdplot</span>(</span>
<span id="cb48-26"><a href="#cb48-26" tabindex="-1"></a>        <span class="at">y     =</span> df_rdd48<span class="sc">$</span>cabap,</span>
<span id="cb48-27"><a href="#cb48-27" tabindex="-1"></a>        <span class="at">x     =</span> df_rdd48<span class="sc">$</span>running_var,</span>
<span id="cb48-28"><a href="#cb48-28" tabindex="-1"></a>        <span class="at">covs  =</span> <span class="fu">cbind</span>(df_rdd48<span class="sc">$</span>age, df_rdd48<span class="sc">$</span>female, df_rdd48<span class="sc">$</span>income, df_rdd48<span class="sc">$</span>education, df_rdd48<span class="sc">$</span>psu_rul),</span>
<span id="cb48-29"><a href="#cb48-29" tabindex="-1"></a>        <span class="at">x.lim =</span> cutoff_range,</span>
<span id="cb48-30"><a href="#cb48-30" tabindex="-1"></a>        <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb48-31"><a href="#cb48-31" tabindex="-1"></a>        <span class="at">y.lab =</span> <span class="st">&quot;Cabinet approval&quot;</span>,</span>
<span id="cb48-32"><a href="#cb48-32" tabindex="-1"></a>        <span class="at">title =</span> titles[i],</span>
<span id="cb48-33"><a href="#cb48-33" tabindex="-1"></a>        <span class="at">c     =</span> cutoff_diffs[i]</span>
<span id="cb48-34"><a href="#cb48-34" tabindex="-1"></a>      )</span>
<span id="cb48-35"><a href="#cb48-35" tabindex="-1"></a>    })</span>
<span id="cb48-36"><a href="#cb48-36" tabindex="-1"></a>  }, <span class="at">type =</span> <span class="st">&quot;message&quot;</span>))  </span>
<span id="cb48-37"><a href="#cb48-37" tabindex="-1"></a>  </span>
<span id="cb48-38"><a href="#cb48-38" tabindex="-1"></a>  plots[[i]] <span class="ot">&lt;-</span> rd<span class="sc">$</span>rdplot <span class="sc">+</span> <span class="fu">theme_igray</span>()</span>
<span id="cb48-39"><a href="#cb48-39" tabindex="-1"></a>}</span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" 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<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" 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<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<div class="sourceCode" id="cb60"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb60-1"><a href="#cb60-1" tabindex="-1"></a>ordered_plots1 <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb60-2"><a href="#cb60-2" tabindex="-1"></a>  plots[[<span class="dv">1</span>]], plots[[<span class="dv">2</span>]],  </span>
<span id="cb60-3"><a href="#cb60-3" tabindex="-1"></a>  plots[[<span class="dv">3</span>]], plots[[<span class="dv">4</span>]],  </span>
<span id="cb60-4"><a href="#cb60-4" tabindex="-1"></a>  plots[[<span class="dv">5</span>]], plots[[<span class="dv">6</span>]]</span>
<span id="cb60-5"><a href="#cb60-5" tabindex="-1"></a>)</span>
<span id="cb60-6"><a href="#cb60-6" tabindex="-1"></a></span>
<span id="cb60-7"><a href="#cb60-7" tabindex="-1"></a>ordered_plots2 <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb60-8"><a href="#cb60-8" tabindex="-1"></a>  plots[[<span class="dv">6</span>]], plots[[<span class="dv">7</span>]], </span>
<span id="cb60-9"><a href="#cb60-9" tabindex="-1"></a>  plots[[<span class="dv">8</span>]], plots[[<span class="dv">9</span>]], </span>
<span id="cb60-10"><a href="#cb60-10" tabindex="-1"></a>  plots[[<span class="dv">10</span>]], plots[[<span class="dv">11</span>]]  </span>
<span id="cb60-11"><a href="#cb60-11" tabindex="-1"></a>)</span></code></pre></div>
</div>
<div id="figure-d7-gradually-shifting-the-cutoff-before-the-second-gold-medal-does-not-alter-the-null-results-for-cabinet-approval" class="section level3 unnumbered">
<h3 class="unnumbered">Figure D7: Gradually shifting the cutoff (Before
the second gold medal) does not alter the null results for cabinet
approval</h3>
</div>
<div id="figure-d8-gradually-shifting-the-cutoff-after-the-second-gold-medal-does-not-alter-the-null-results-for-cabinet-approval" class="section level3 unnumbered">
<h3 class="unnumbered">Figure D8: Gradually shifting the cutoff (After
the second gold medal) does not alter the null results for cabinet
approval</h3>
<div class="sourceCode" id="cb61"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb61-1"><a href="#cb61-1" tabindex="-1"></a>cutoff_diffs <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">5</span>, <span class="sc">-</span><span class="dv">3</span>, <span class="sc">-</span><span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">15</span>, <span class="dv">20</span>)</span>
<span id="cb61-2"><a href="#cb61-2" tabindex="-1"></a>titles <span class="ot">&lt;-</span> <span class="fu">c</span>( <span class="st">&quot;5 mins before&quot;</span>, <span class="st">&quot;3 mins before&quot;</span>, <span class="st">&quot;1 min before&quot;</span>,</span>
<span id="cb61-3"><a href="#cb61-3" tabindex="-1"></a>             <span class="st">&quot;At cutoff&quot;</span>, <span class="st">&quot;1 min after&quot;</span>, <span class="st">&quot;3 mins after&quot;</span>, <span class="st">&quot;5 mins after&quot;</span>, <span class="st">&quot;15 mins after&quot;</span>, <span class="st">&quot;20 mins after&quot;</span>)</span>
<span id="cb61-4"><a href="#cb61-4" tabindex="-1"></a></span>
<span id="cb61-5"><a href="#cb61-5" tabindex="-1"></a>plots <span class="ot">&lt;-</span> <span class="fu">list</span>()</span>
<span id="cb61-6"><a href="#cb61-6" tabindex="-1"></a></span>
<span id="cb61-7"><a href="#cb61-7" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(cutoff_diffs)) {</span>
<span id="cb61-8"><a href="#cb61-8" tabindex="-1"></a>  cutoff_range <span class="ot">&lt;-</span> <span class="fu">range</span>(df_rdd66<span class="sc">$</span>running_var2)</span>
<span id="cb61-9"><a href="#cb61-9" tabindex="-1"></a>  </span>
<span id="cb61-10"><a href="#cb61-10" tabindex="-1"></a>  rd <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rdd66<span class="sc">$</span>cabap, df_rdd66<span class="sc">$</span>running_var2,</span>
<span id="cb61-11"><a href="#cb61-11" tabindex="-1"></a>               <span class="at">covs =</span> <span class="fu">cbind</span>(df_rdd66<span class="sc">$</span>age, df_rdd66<span class="sc">$</span>female, df_rdd66<span class="sc">$</span>income, df_rdd66<span class="sc">$</span>education, df_rdd66<span class="sc">$</span>psu_rul),</span>
<span id="cb61-12"><a href="#cb61-12" tabindex="-1"></a>               <span class="at">x.lim =</span> cutoff_range,</span>
<span id="cb61-13"><a href="#cb61-13" tabindex="-1"></a>               <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb61-14"><a href="#cb61-14" tabindex="-1"></a>               <span class="at">y.lab =</span> <span class="st">&quot;Cabinet approval&quot;</span>,</span>
<span id="cb61-15"><a href="#cb61-15" tabindex="-1"></a>               <span class="at">title =</span> titles[i],</span>
<span id="cb61-16"><a href="#cb61-16" tabindex="-1"></a>               <span class="at">c =</span> cutoff_diffs[i])</span>
<span id="cb61-17"><a href="#cb61-17" tabindex="-1"></a>  </span>
<span id="cb61-18"><a href="#cb61-18" tabindex="-1"></a>  p <span class="ot">&lt;-</span> rd<span class="sc">$</span>rdplot <span class="sc">+</span> <span class="fu">theme_igray</span>()</span>
<span id="cb61-19"><a href="#cb61-19" tabindex="-1"></a>  </span>
<span id="cb61-20"><a href="#cb61-20" tabindex="-1"></a>  plots[[i]] <span class="ot">&lt;-</span> rd<span class="sc">$</span>rdplot</span>
<span id="cb61-21"><a href="#cb61-21" tabindex="-1"></a>}</span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
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bXxlsCFaAJqQXzrBDSsNt4SuBBNQC2Ib52AhtXGWwIXogmoBfGtE9Cw2nhL4EI0AbUgvnUCGlYbbwlciCagFsS3TkDDauMtgQvRBNSC+NYJaFhtvCVwIZqAWhDfOgENq423BC5EE1AL4lsnoGG18ZbAhWgCakF86wQ0rDbeErgQTUAtiG+dgIbVxlsCF6IJqAXxrRPQsNp4S+BCNAG1IL51AhpWG28JXIgmoBbEt05Aw2rjLYEL0QTUgvjWCWhYbbwlcCGagFoQ3zoBDauNtwQuRBNQC+JbJ6BhtfGWwIVoAmpBfOsENKw23hK4EE1ALYhvnYCG1cZbAheiCagF8a0T0LDaeEvgQjQBtSC+dQIaVhtvCVyIJqAWxLdOQMNq4y2BC9EE1IL41gloWG28JXAhmoBaEN86AQ2rjbcELkQTUAviWyegYbXxlsCFaAJqQXzrBDSsNt4SuBBNQC2Ib52AhtXGWwIXogmoBfGtE9Cw2nhL4EI0AbUgvnUCGlYbbwlciCagFsS3TkDDauMtgQvRBNSC+NYJaFhtvCVwIZqAWhDfOgENq423BC5EE1AL4lsnoGG18ZbAhWgCakF86wQ0rDbeErgQTUAtiG+dgIbVxlsCF6IJqAXxrRPQsNp4S+BCNAG1IL51AhpWG28JXIgmoBbEt05Aw2rjLYEL0QTUgvjWCWhYbbwlcCGagFoQ3zoBDauNtwQuRBNQC+JbJ6BhtfGWwIVoAmpBfOsENKw23hK4EB0+oN/f+NmfViC+iySgYbXxlsCF6OgBffbREQHdBjmmNt4SuBAdPaD3jwjoVsgxtfGWwIXo4AH9/gYB3Q45pjbeErgQHTug4xvwf899oFshx9TGWwIXomMH9M7RazyItB1yTG28JXAhOnRAH45vvqcB/XEz37qmqnz7DMMwW5k+A/r05rXfjAgowzBRps+A3jn6JeeBboscUxtvCVyIDnwT/v7k8XcCuh1yTG28JXAhOm5Av78xvgFPQLdFjqmNtwQuRMcN6P2j+fzkd0uQ7AudHhMBjaqNtwQuRBNQAroVckxtvCVwITpuQGfDTfjtkGNq4y2BC9EElIBuhRxTG28JXIgmoAR0K+SY2nhL4EI0ASWgWyHH1MZbAheiwwfUhPjWCWhYbbwlcCGagFoQ3zoBDauNtwQuRBNQC+JbJ6BhtfGWwIVoAmpBfOsENKw23hK4EE1ALYhvnYCG1cZbAheiCagF8a0T0LDaeEvgQjQBtSC+dQIaVhtvCVyIJqAWxLdOQMNq4y2BC9EE1IL41gloWG28JXAhmoBaEN86AQ2rjbcELkQTUAviWyegYbXxlsCFaAJqQXzrBDSsNt4SuBBNQC2Ib52AhtXGWwIXogmoBfGtE9Cw2nhL4EI0AbUgvnUCGlYbbwlciCagFsS3TkDDauMtgQvRBNSC+NYJaFhtvCVwIZqAWhDfOgENq423BC5EE1AL4lsnoGG18ZbAhWgCakF86wQ0rDbeErgQTUAtiG+dgIbVxlsCF6IJqAXxrRPQsNp4S+BCNAG1IL51AhpWG28JXIgmoBbEt05Aw2rjLYEL0QTUgvjWCWhYbbwlcCGagFoQ3zoBDauNtwQuRBNQC+JbJ6BhtfGWwIVoAmpBfOsENKw23hK4EE1ALYhvnYCG1cZbAheiCagF8a0T0LDaeEvgQjQBtSC+dQIaVhtvCVyIJqAWxLdOQMNq4y2BC9EE1IL41gloWG28JXAhmoBaEN86AQ2rjbcELkQTUAviWyegYbXxlsCFaAJqQXzrBDSsNt4SuBBNQC2Ib52AhtXGWwIXogmoBfGtE9Cw2nhL4EI0AbUgvnUCGlYbbwlciCagFsS3TkDDauMtgQvRBNSC+NYJaFhtvCVwIZqAWhDfOgENq423BC5EE1AL4lsnoGG18ZbAhWgCakF86wQ0rDbeErgQTUAtiG+dgIbVxlsCF6IJqAXxrRPQsNp4S+BCNAG1IL51AhpWG28JXIgmoBbEt05Aw2rjLYEL0QTUgvjWCWhYbbwlcCGagFoQ3zoBDauNtwQuRBNQC+JbJ6BhtfGWwIVoAmpBfOsENKw23hK4EE1ALYhvnYCG1cZbAheiCagF8a0T0LDaeEvgQjQBtSC+dQIaVhtvCVyIJqAWxLdOQMNq4y2BC9EE1IL41gloWG28JXAhmoBaEN86AQ2rjbcELkQTUAviWyegYbXxlsCFaAJqQXzrBDSsNt4SuBBNQC2Ib52AhtXGWwIXogmoBfGtE9Cw2nhL4EI0AbUgvnUCGlYbbwlciCagFsS3TkDDauMtgQvRBNSC+NYJaFhtvCVwIZqAWhDfOgENq423BC5EE1AL4lsnoGG18ZbAhWgCakF86wQ0rDbeErgQTUAtiG+dgIbVxlsCF6IJqAXxrRPQsNp4S+BCNAG1IL51AhpWG28JXIgmoBbEt05Aw2rjLYEL0QTUgvjWCWhYbbwlcCGagFoQ3zoBDauNtwQuRBNQC+JbJ6BhtfGWwIVoAmpBfOsENKw23hK4EE1ALYhvnYCG1cZbAheiCagF8a0T0LDaeEvgQjQBtSC+dQIaVhtvCVyIJqAWxLdOQMNq4y2BC9EE1IL41gloWG28JXAhmoBaEN86AQ2rjbcELkQTUAviWyegYbXxlsCFaAJqQXzrBDSsNt4SuBBNQC2Ib52AhtXGWwIXogmoBfGtE9Cw2nhL4EI0AbUgvnUCGlYbbwlciCagFsS3TkDDauMtgQvRBNSC+NYJaFhtvCVwIZqAWhDfOgENq423BC5EE1AL4lsnoGG18ZbAhWgCakF86wQ0rDbeErgQTUAtiG+dgIbVxlsCF6IJqAX51jVV5dtnGIbZynANtMtwDXRwNN4CuBDNNVAL4lsnoGG18ZbAhWgCakF86wQ0rDbeErgQTUAtiG+dgIbVxlsCF6IJqAXxrRPQsNp4S+BCNAG1IL51AhpWG28JXIgmoBbEt05Aw2rjLYEL0QTUgvjWCWhYbbwlcCGagFoQ3zoBDauNtwQuRBNQC+JbJ6BhtfGWwIVoAmpBfOsENKw23hK4EE1ALYhvnYCG1cZbAheiSwvo2ftvrc7P73WE5BxZckwENKo23hK4EF1aQJ9cr1bnR7c7QnKOLDkmAhpVG28JXIgmoBYk58iSYyKgUbXxlsCF6NIC2ssQUD85pjbeErgQTUAtiG+dgIbVxlsCF6IJqAXxrRPQsNp4S+BCdMEBPfvDbO7+5e2OkM6HtTAENKw23hK4EF1qQB+/x4NIBFSAxlsAF6ILDejig/GXbneE5BxZckwENKo23hK4EF1oQE+q6uDlNw/r/1UH73SG5BxZckwENKo23hK4EF1mQM9uVZfu1f//bt3SSx2fiERAeyDH1MZbAheiywzok+sHH47qdl4d//9xndFukJwjS46JgEbVxlsCF6JLDejkcaPT6vL8/ztBMg4sPSYCGlUbbwlciC48oPWt9yfXu96GJ6B+ckxtvCVwIbrMgJ7dmtyEf3RYd3RW0y6QnCNLjomARtXGWwIXossM6Oh4cu/n9K7QaUY7QXKOLDkmAhpVG28JXIguNKCPDqtXvqgfhr9ax5Sb8MOTY2rjLYEL0YUGdFzN+vlHp1V1cFhNro12guQcWXJMBDSqNt4SuBBdakBHv69vuJ8dT56IxHmgw5NjauMtgQvRxQZ0NPr3cTfPvrpy5e2u/SSgPZBjauMtgQvRBQc0fwionxxTG28JXIgmoBbEt05Aw2rjLYEL0WUG9Mn1V77IufQG4tgdEdAJOaY23hK4EF1qQKvqoPt9n3NI7uJ0CGhYbbwlcCG6zICefXxYP/z+0gc5CALaBzmmNt4SuBBdZkDHc/eNOqEHWTflCaifHFMbbwlciC42oPUpTC/WDX3hHU5jEpBjauMtgQvRBQd0PN99Orkp/+c8lXNwckxtvCVwIbrsgI6m7y7Hi4kMT46pjbcELkQXHtCzT1/kXTkl5JjaeEvgQnTJAT27+3renaAE1E+OqY23BC5ElxvQr/MfhiegfnJMbbwlcCG60IA+fm/y6NHFvBNBCaifHFMbbwlciC4zoPUzkRxPRSKgfnJMbbwlcCG62IC+8nnOxc8g+av1ENCw2nhL4EJ0mQE9+7fs58FPIJ5lAjohx9TGWwIXossMqHMIqJ8cUxtvCVyILjeg3316paoOrrz9TQak+0o6BDSsNt4SuBBdbEBPqma6vqUcAe2DHFMbbwlciC41oHU/X3j5rTdfzCkoAfWTY2rjLYEL0YUG9NFhdWl6Bv3jW9XBh10h3Y9r4ZgIaFRtvCVwIbrQgB6372V8dqu63BXS9agWh4CG1cZbAheiywzo2a3kWuejw65vDE9A/eSY2nhL4EJ0mQF9cj15AaaF32wG6fbhy0NAw2rjLYEL0QTUgnT78OUhoGG18ZbAhegyA3p2q3p3/pvTipvww5NjauMtgQvRZQaUB5FmcB05pjbeErgQXWhAHx1WF6evJvL165zGpCDH1MZbAheiCw3o9IlIV65cyXoqEgH1k2Nq4y2BC9GlBnT01eHsmZwH73SHdN5YGAIaVhtvCVyILjago7O7b46vgb78QcYL2xFQPzmmNt4SuBBdbkAdQ0D95JjaeEvgQnShAT3OeS+5FuLYHRHQCTmmNt4SuBBdZkCfXE/OA82AOHZHBHRCjqmNtwQuRJca0K5PPlqEOHZHBHRCjqmNtwQuRJcZ0IUXE8mAOHZHBHRCjqmNtwQuRJcZ0NFJ83KgWUNA/eSY2nhL4EJ0oQH97pPJC9LP5uc8F35wckxtvCVwIbrMgD65XqXDqzENT46pjbcELkQTUAuSc2TJMRHQqNp4S+BCdJkBdQ4B9ZNjauMtgQvRBNSC+NYJaFhtvCVwIbrsgP4hB0FA+yDH1MZbAheiyw3o3Tcmr8WU85xOAuonx9TGWwIXoksN6Nmt+UNIr3Z+PSYC6ifH1MZbAheiCw1o3c+Dl//ps39587Dq/I4eBLQHckxtvCVwIbrQgJ5U1V9Mr3iefVJ1fmERAuonx9TGWwIXossM6PgKaPs+Hse8qZyAHFMbbwlciC4zoE+uJy8m8uiQE+mHJ8fUxlsCF6JLDWjSzO6vbUdA/eSY2nhL4EJ0mQFdeDm7R4eXeDGRwckxtfGWwIXoMgM6Oknu9zzhPlABOaY23hK4EF1oQM9uzU//POn8WiIEtAdyTG28JXAhusyAnr1fn//58tuf/Wv960tdXxWUgPrJMbXxlsCF6DIDuvRydl1f1I6A+skxtfGWwIVoAmpBco4sOSYCGlUbbwlciC4zoM4hoH5yTG28JXAhmoBaEN86AQ2rjbcELkSXHVBeD1RGjqmNtwQuRJcbUF4PlIAK0HgL4EJ0qQHl9UAncB05pjbeErgQXWhAeT3QKVxHjqmNtwQuRBcaUF4PdArXkWNq4y2BC9FlBnTD1wP9498dHV37xW9XITlHlhwTAY2qjbcELkSXGdDNXg/0y6PJXPvNCiTnyJJjIqBRtfGWwIXoUgO6weuBPjy69g+j0Q8fHf3kd8uQnCNLjomARtXGWwIXossM6CavB/rso6Nf1b8+vTn9NYXkHFlyTAQ0qjbeErgQXWZAN3k90Kc3Z9c87xz9chmSc2TJMRHQqNp4S+BCdKEB7fJ6oAR0G+SY2nhL4EJ0mQHt8nqgT2/OH0X6cTPfuqaqfPsMwzBbmf5fzu7+0WttnQkowzDlTu8BfchpTFshx9TGWwIXosu8Cb/5PLxxbfkxeALaBzmmNt4SuBAdPKD3jeufBLQPckxtvCVwITp2QL80+0lAeyDH1MZbAheiCw7o2R9mc/cvb5sf8OzO0U+Xn4Q0heQcWXJMBDSqNt4SuBBdakAfv/f8x9/vHP3sTzYk58iSYyKgUbXxlsCF6EIDuvgw/KXb1sfcX9dPAtoDOaY23hK4EF1oQE/q11N+87D+X3XwjvkhT28eNfPa0l8RUD85pjbeErgQXWZAz25Vl+7V//9u3VLztURGD48I6BbJMbXxlsCF6DIDOns90JPJyyof84r0AnJMbbwlcCG61IBOHjc6nbwO02nnN0UioH5yTG28JXAhuvCA1rfen1y3b8OfA8k5suSYCGhUbbwlcCG6zIDOXlB5+mYe616R/hxIzpElx0RAo2rjLYEL0WUGdHQ8ufdzelfo2vdEWg/JObLkmAhoVG28JXAhutCAPjqsXvli9uacx2sehj8HknNkyTER0KjaeEvgQnShAR1Xs37+0WlVHRxWyVscbwjJObLkmAhoVG28JXAhutSAjn5f33A/O548EanjFVAC2gM5pjbeErgQXWxAR6N/H3fz7KsrV97u2k8C2gM5pjbeErgQXXBA84eA+skxtfGWwIVoAmpBfOsENKw23hK4EE1ALYhvnYCG1cZbAheiCagF8a0T0LDaeEvgQjQBtSC+dQIaVhtvCVyIJqAWxLdOQMNq4y2BC9EE1IL41gloWG28JXAhmoBaEN86AQ2rjbcELkQTUAviWyegYbXxlsCF6DIDevb+z9unHz168895MafrwSoAACAASURBVJHByTG18ZbAhegyA7rwEqC8HqiCHFMbbwlciA4QUF4PVEGOqY23BC5EFxfQxXeEn70vPDfhByfH1MZbAheiiwto/Rqgy8O7cg5PjqmNtwQuRJcX0LP/+9Zbbx4evPxWM3/1eWdIzpElx0RAo2rjLYEL0eUFtJ7ujxstQhy7IwI6IcfUxlsCF6LLDOjCaUwZEMfuiIBOyDG18ZbAhegyAzqbs29yEAS0D3JMbbwlcCG63IDefaN+X7kn/7n7O3oQ0B7IMbXxlsCF6FIDevZx/fD7OKDXq4u3O0O6H9fCMRHQqNp4S+BCdKkBPa6qi//l8Ee3z/4H78opIcfUxlsCF6ILDehpVb0zeyz+q0POAxWQY2rjLYEL0YUG9Li6Oj+Z6aS63BWScWDpMRHQqNp4S+BCdJkBPbt18OE8oDwXXkGOqY23BC5ElxnQaTpnAeXVmBTkmNp4S+BCNAG1IBkHlh4TAY2qjbcELkSXGdCzW/UDR7NynvJqTAJyTG28JXAhusyATh84mgZ0HFMeRBqeHFMbbwlciC40oI8Oq1fvTQL6+PWqfkCpGyTnyJJjIqBRtfGWwIXoQgM6vgpaVVcOD15+cfzr1c6QjANLj4mARtXGWwIXoksN6Oj3h83LKXfuJwHtgRxTG28JXIguNqCj7z69Mq7nC698kQHpvpIOAQ2rjbcELkSXG1DHEFA/OaY23hK4EE1ALYhvnYCG1cZbAheiSw7oH5rp+rrKBNRPjqmNtwQuRJca0MfvJe/KyTORhifH1MZbAheiCw3o4rvDE9DhyTG18ZbAhehCA3pSVRf/6rNm/o2ncg5OjqmNtwQuRJcZ0Iynby5AHLsjAjohx9TGWwIXossM6JPrnZ++uQBx7I4I6IQcUxtvCVyILjWgXe/2XIQ4dkcEdEKOqY23BC5ElxnQ6SvSZw8B9ZNjauMtgQvRZQZ0dJLxDPgE4tgdEdAJOaY23hK4EF1oQMdXQd/JufgZJH+1HgIaVhtvCVyILjOgZ++/WVUHL781m59zGtPg5JjaeEvgQnSZAV08j54T6QXkmNp4S+BCNAG1IDlHlhwTAY2qjbcELkSXGVDnEFA/OaY23hK4EE1ALYhvnYCG1cZbAheiCagF8a0T0LDaeEvgQnRpAT17v37Mffz/6fAo/PDkmNp4S+BCdGkBfXK9fsiIB5FmcB05pjbeErgQTUAtSM6RJcdEQKNq4y2BC9GlBbSXIaB+ckxtvCVwIZqAWhDfOgENq423BC5EFx3Qsz/kIAhoH+SY2nhL4EJ0uQG9+8bk/s9XvsiAdF9Jh4CG1cZbAheiSw3o2a35Q0ivdjyJiYD2QY6pjbcELkQXGtC6nwcv/+Nn//LmuKCd3x6JgPrJMbXxlsCF6EIDelo1r6h89klVvdsV0vmwFoaAhtXGWwIXossM6PgKaPuK9Medr4ISUD85pjbeErgQXWZAF96V89EhJ9IPT46pjbcELkSXGtCkmd3fopOA+skxtfGWwIXoMgO68K6cjw4v8WIig5NjauMtgQvRZQZ0dJLc79n9HToJqJ8cUxtvCVyILjSgT65XfzG72nnS+bVECGgP5JjaeEvgQnRpAW1eCfTN6Xmg//rWYVW9zOuBDk+OqY23BC5ElxbQpRey4+XsVOSY2nhL4EI0AbUgOUeWHBMBjaqNtwQuRJcW0F6GgPrJMbXxlsCFaAJqQXzrBDSsNt4SuBBNQC2Ib52AhtXGWwIXogsO6NkfZnP3L293hHQ+rPk8eEBAJ+SY2nhL4EJ0qQF9/J7mQSQCOiPH1MZbAheiCw3o4oPxl253hOQc2XQI6IwcUxtvCVyILjSgJ/V59G8e1v+rDt7pDMk5sukQ0Bk5pjbeErgQXWZAz25Vl+7V//9u3dKuryVCQHsgx9TGWwIXossM6Oz1QKcvI3I85CvSE9AZOaY23hK4EF1qQCePG51OXpPpdMhXpB8H9EJVXWj/IOh3VlBtvCVwIbrwgNa33p9cH/D1QB88uFAHtC1o0O+soNp4S+BCdJkBnb2g8vTNPAZ9RfomoPOCBv3OCqqNtwQuRJcZ0NHx5N7P6V2hg74n0jygTUGDfmcF1cZbAheiCw3oo8PqlS9mb8553PlheALqJ8fUxlsCF6ILDei4mvXzj06r6uCw6v6WHt9mTxvQ/MtgGIbZymz8XPjf1zfcz44nT0Qa9DxQroFOyDG18ZbAhehSr4GO59/H3Tz76sqVt7v2kweReiDH1MZbAheiCw5o/nAak58cUxtvCVyIJqAWJH+VE+ln5JjaeEvgQnSZAf3um+mvj176oPPt9xFP5eyDHFMbbwlciC4xoI9fbx53r1+UqfNrMRHQPsgxtfGWwIXoAgP61WHVPPv9k/pB+K4nMXkC+oCAzsgxtfGWwIXo8gJ6Om7mxc9nvzn7ePy7ri/GREB7IMfUxlsCF6KLC2j9WvTpdc6T7u/o4QvoAwI6CquNtwQuRBcX0JOlE+fPbmU8E6n7cc2GgDbkmNp4S+BCdGkBrXu5eJP9tPtTkQionxxTG28JXIguLaDjW/CTV7Jr59HhgO/KSUAbckxtvCVwIbrAgC7lcvVPng/pelTzIaANOaY23r1O8poS58K3wd5sCKgJ6XpU8yGgDTmmNt59zoULmxW0NO9N2TlLG9wHunoTnvtAhyfH1Ma7x7lwYcOCFua9MTtn6bmPwh8vP+h+Ug34pnIEtCHH1Ma7v7lwYdOCluW9OTtn6bkBXX7QndOYNOSY2nj3NwT0eeycpc4n0h9zIr2EHFMb7/6GgD6PnbO02VM5X22ugz5+L+fJ8ATUT46pjXd/Q0Cfx85Z2uDFRE7qVxC5+I+fffbZp6/X/9n1HlAC2gc5pjbePQ4PIj2HnbO0ycvZ/f6waufgrzMgOUc2GQLakGNq493ncBrT+eycpY1eUHlyw32az1e/yYFk7EyHgDbkmNp49zqcSH8uO2dp07f0+Ppf33/rrz7LqeeIgPZBjqmNtwQuRJcaUNcQUD85pjbeErgQTUAtSPYmAW3IMbXxlsCFaAJqQbI3CWhDjqmNtwQuRBNQC5K9SUAbckxtvCVwIZqAWpDsTQLakGNq4y2BC9EE1IJkbxLQhhxTG28JXIgmoBYka6s+aY2ANuSY2nhL4EI0AbUgOUsXCGhKjqmNtwQuRBNQC5Kxc4GALpBjauMtgQvRBNSCdF+5QEAXyTG18ZbAhWgCakG6rxDQJXJMbbwlcCGagFqQ7isEdIkcUxtvCVyIJqAWpPsKAV0ix9TGWwIXogmoBcnYIaCL5JjaeEvgQjQBtSA5SwR0gRxTG28JXIgmoBYka4uApuSY2nhL4EI0AbUg2ZsEtCHH1MZbAheiCagFyd4koA05pjbeErgQTUAtSPYmAW3IMbXxlsCFaAJqQbI3CWhDjqmNtwQuRBNQC5K9SUAbckxtvCVwIZqAWpDsTQLakGNq4y2BC9EE1IJkbxLQhhxTG28JXIgmoBYke7MO6IWqutD+SdDvrKDaeEvgQvRadn1a+JbZOUt7EtD2Uxf0OyuoNt4SuBC9jj19avd22TlL+xLQ+acu6HdWUG28JXAheg179uJCWy1o2QFtPnVBv7OCauMtgQvRNvvChQEKSkC3PgR0cDTeArgQTUAtSPYmAW3IMbXxlsCFaAJqQbI3CWhDjqmNtwQuRBNQC5K9yYNIDTmmNt4SuBDNg0gWJHuT05gackxtvCVwIZrTmCxI9iYn0jfkmNp4S+BCNCfSW5DsTZ7K2ZBjauMtgQvRPJXTgmRvEtCGHFMbbwlciCagFiR7k4A25JjaeEvgQjQBtSDZmwS0IcfUxlsCF6IJqAXJ3iSgDTmmNt4SuBBNQC1I9iYBbcgxtfGWwIVoAmpBsjcJaEOOqY23BC5EE1ALkrt4ofCAbn5u2xa0O8B7Z288RX25u6DxHp6ds7TLAX3woPCAdnh2Rf/aXeB9szefkr7cndB4D8/OWdqDgF4oNaBdnt/bu3YneM/sDlPQl7sbGu/h2TlLOxzQC3sQUM/zyzq9wkzf2t3g/bK7zG59uQdE4z08O2eJgHaZZbbrFQ4I6CboXfpyD4nGe3h2ztJ+BHRHX0zE9xpbBHQT9A59uQdF4z08O2dpTwK6vZez6xTARbbzVV4J6CZoQiKAC9EE1ILkraUB7f0FlR90mga+cBHOgPIg0iZoQiKAC9EE1ILkrS0ENPstPbqVsuv0cC/oRh/KaUzpeB676wI3/3QQOAEVsHOWigzoRumrP3DT65BdktrFcPOfRU6kT8b1r1YXuPWHw8AJqICds7TbAd3oTeXmf9m9aF1vhK88iNRnTc8n8wM1H98dJ13gxp8NBBd9vbtcU9hkOh8AATUheWubBdSK2KYEZ0CtayNbyikBnY/3rucO8NU/Ggo+wNe771huMrvgvZ6ds7Q3AW3+cP4ZzvnyLI83oM+7Gez4TlomE9BmCGjebF643tjdW0pATUjeWhrQ+R9+++3Kl8LxDd3xp8H31fW0lIDOh4BuPBvE0oT3wd74eBbRBNSC5K0lAZ3/yfIn3vsN3W132/80n0MmoM0Q0HMmK5gr8Dx2p1lzdATUhOStzQI6eTWmdd8I7m/oTqvb+Opu9u1OQNsZqp978yBSD81chOet5U1Px9zDFBzQ5JO79J013NWRerb8nXXOjwEBTWaoL/hOn8bUazKX4H1cSMfZgYiWHdDmD5e/s4bs55DfWVv76ciYXQto2BPp+72iuR7e8+V1QX+r+34vOKDJH66exjRcP0XfWfKW7lxAh4IL0S17oGqm8O1d9HPRE7bmez1oQIe6OjKBD8QxyDVaVVL5D1TPs+l3jNB78Gimsytf7334Ni8goAOOOKDJDPrjtSs/UD3NxrdZhvaWVjOZHft6D/WZIKBbn90J6HyG+JHbsR8o52x+r7nkdJ7ZxPw2X/v1HiKiBHTrs4MBTWZr11928AcqfzqctzHsCeULX6ug3+bne2+5ogR067PbAZ3P838+O8JzF/1TUEA7fVGCfps/33uLESWgW589CWg6fbR03bmYmYfUCb3nAc399Af9Nt/QezsVJaBbnz0MaDLZLV37bCD/MT0XvYcB7eMGQNBv8w7e/SeUgG599jugyXT7GV//fPReD8pE78uDSJt+Rjc9ASDmt3lX714rSkC3PsUENJl1P/nJd6V56u2uPKWx+/R1GtPzP3H57KDf5p537PGzc5YIaJcpMaDprI9pzy/hsvHs1on0m35+ziVvdu9BzG/zTO9+EkpAtz6lB3RhzqvFfgd0Y/h5n4KcH9jN/+EJ+m3u8PZfESWgW59QAU3go+e/nvm20MN6b9mSgD4X7WB7v0gEdOsTOKAL87zO9JdUxX2/2/tngYA+F+1lO75y+oA++/WNo6Nf/HYVkndxBDQh75j2cgY2ylFGl5ze3Q5r+ciEp1Dt2td7MLSfnZ1QeUCf3jyq5ye/W4HkXR4BTci7pv3cCnRu19CT5+0bHkR6Hron9oZf4UV2DqjPgN45+tlvRz98dPSzPy1Dsi7uwoMHk7csTt4TaRT2O2vdW1sMA+/58naqkueM8BSqdd/mw5x8u+bPffCB/+FY/A7Y6Fp/DqbHgH5/Y3Ld8+nNa79ZhuRc3IU0oOm7cuYfoXt2KqBDPRloe9fENkGX9uX2eQ906oP9xz748HddJAndCK4O6P2j12a//nIZknFpFxYDuvq+8IrZpYAOdSLRaIv3BW6AjvnlPuetTAb4mp8Xb9/b3z4/Ylu5tbMZXB3QO0e/mvz6cBbSBNL9wi4sB7SRD/oTteadTIYpaN/aHY496Jf7vDfT2/7X/Nx458E33d7C1ztN6PnsnEvvL6DPPprddP/+RnMn6I+b+bbzrAa0+2UUPe13pPpIus8+H7typJ83H1x66A/mDe3/sgnons4+R2ifj105BDRvLlxo7g7t/7K3EtDlE5m4Ce8ncxN++OEmfG/wTbe39toHF5KHlNaxcy57q9dA55CMi1sOaPPnQX+ieBBJMLsWUB5EypwGfn5CywoopzEtkjmNafjZuYByGpMbfk5Ci3oUfsSJ9Ivkkk6kH3V4RbmYX25OpO95EvjahKoD2pz/2c95oCOeyrlAjqmNtwQuRA/BXnNLXh3Qnp+JREAXyDG18ZbAhehh2GZC1QF99tHRT/t8LjwBTckxtfGWwIXowdirCVUHdPQDr8a0PXJMbbwlcCF6QPZyQuUBHf3w63E/f7F8/ZOA9kGOqY23BC5ED8pevCWvD+haSN4aAU3IMbXxlsCF6IHZaUIJ6NaHgA6OxlsAF6KHZ88TSkC3PgR0cDTeArgQrWDPEkpAtz4EdHA03gK4EK1hT66CEtCtDwEdHI23AC5EK9k5SwS0yxDQwdF4C+BCNAG1IHlrBDQhx9TGWwIXogmoBclbI6AJOaY23hK4EE1ALUjeGgFNyDG18ZbAhWgCakHy1ghoQo6pjbcELkQTUAuSt0ZAE3JMbbwlcCGagFqQvDUCmpBjauMtgQvRBNSC5K0R0IQcUxtvCVyIJqAWJG+NgCbkmNp4S+BCNAG1IHlrBDQhx9TGWwIXogmoBclbI6AJOaY23hK4EE1ALUjeGgFNyDG18ZbAhWgCakHy1ghoQo6pjbcELkQTUAuSt0ZAE3JMbbwlcCGagFqQvDUCmpBjauMtgQvRBNSC5K09mL1hCQENq423BC5EE1ALkrc2qScBnZJjauMtgQvRBNSC5K3NAvqAgIbVxlsCF6IJqAXJW5u+1RMBnZBjauMtgQvRBNSC5K0R0IQcUxtvCVyIJqAWJG+NgCbkmNp4S+BCNAG1IHlrBDQhx9TGWwIXogmoBclbI6AJOaY23hK4EE1ALUjeGgFNyDG18ZbAhWgCakHy1ghoQo6pjbcELkQTUAuSt0ZAE3JMbbwlcCGagFqQvDUCmpBjauMtgQvRBNSC5K0R0IQcUxtvCVyIJqAWJG+NgCbkmNp4S+BCNAG1IHlrBDQhx9TGWwIXogmoBclbI6AJOaY23hK4EE1ALUjeGgFNyDG18ZbAhWgCakHy1ghoQo6pjbcELkQTUAuSt0ZAE3JMbbwlcCGagFqQvDUCmpBjauMtgQvRBNSC5K0R0IQcUxtvCVyIJqAWJG+NgCbkmNp4S+BCNAG1IHlrBDQhx9TGWwIXogmoBclbI6AJOaY23hK4EE1ALUjeGgFNyDG18ZbAhWgCakHy1ghoQo6pjbcELkQTUAuSt0ZAE3JMbbwlcCGagFqQvDUCmpBjauMtgQvRBNSC5K0R0IQcUxtvCVyIJqAWJG+NgCbkmNp4S+BCNAG1IHlrBDQhx9TGWwIXogmoBclbI6AJOaY23hK4EE1ALUjeGgFNyDG18ZbAhWgCakHy1ghoQo6pjbcELkQTUAuSt0ZAE3JMbbwlcCGagFqQvDUCmpBjauMtgQvRBNSC5K0R0IQcUxtvCVyIJqAWJG+NgCbkmNp4S+BCNAG1IHlrBDQhx9TGWwIXogmoBclbI6AJOaY23hK4EE1ALUjeGgFNyDG18ZbAhWgCakHy1ghoQo6pjbcELkQTUAuSt0ZAE3JMbbwlcCGagFqQvDUCmpBjauMtgQvRBNSC5K0R0IQcUxtvCVyIJqAWJG+NgCbkmNp4S+BCNAG1IHlrBDQhx9TGWwIXogmoBclbI6AJOaY23hK4EE1ALUjeGgFNyDG18ZbAhWgCakHy1ghoQo6pjbcELkQTUAuSt0ZAE3JMbbwlcCGagFqQvDUCmpBjauMtgQvRBNSC5K0R0IQcUxtvCVyIJqAWJG+NgCbkmNp4S+BCNAG1IHlrBDQhx9TGWwIXogmoBclbI6AJOaY23hK4EE1ALUjeGgFNyDG18ZbAhWgCakEy9whoS46pjbcELkQTUAuSuUdAW3JMbbwlcCGagFqQzD0C2pJjauMtgQvRBNSCZO4R0JYcUxtvCVyIJqAWJHOPgLbkmNp4S+BCNAG1IJl7BLQlx9TGWwIXogmoBcncI6AtOaY23hK4EE1ALUjmHgFtyTG18ZbAhWgCakEy9whoS46pjbcELkQTUAuSuUdAW3JMbbwlcCGagFqQzD0C2pJjauMtgQvRBNSCZO4R0JYcUxtvCVyIJqAWJHOPgLbkmNp4S+BCNAG1IJl7BLQlx9TGWwIXogmoBcncI6AtOaY23hK4EE1ALUjmHgFtyTG18ZbAhWgCakG+zZtxQOv/q6rMfYZhmG0O10C7DNdAB0fjLYAL0VwDtSCZewS0JcfUxlsCF6IJqAXJ3COgLTmmNt4SuBBNQC1I5h4BbckxtfGWwIVoAmpBMvcIaEuOqY23BC5EE1ALkrlHQFtyTG28JXAhmoBakMw9AtqSY2rjLYEL0QTUgmTuEdCWHFMbbwlciCagFiRzj4C25JjaeEvgQjQBtSCZewS0JcfUxlsCF6IJqAXJ3COgLTmmNt4SuBBNQC1I5h4BbckxtfGWwIVoAmpBMvcIaEuOqY23BC5EE1ALkrlHQFtyTG28JXAhmoBakMw9AtqSY2rjLYEL0QTUgmTuEdCWHFMbbwlciCagFiRzj4C25JjaeEvgQjQBtSCZewS0JcfUxlsCF6IJqAXJ3COgLTmmNt4SuBBNQC1I5h4BbckxtfGWwIVoAmpBMvcIaEuOqY23BC5EE1ALkrlHQFtyTG28JXAhmoBakMw9AtqSY2rjLYEL0QTUgmTuEdCWHFMbbwlciCagFiRzj4C25JjaeEvgQjQBtSCZewS0JcfUxlsCF6IJqAXJ3COgLTmmNt4SuBBNQC1I5h4BbckxtfGWwIVoAmpBMvcIaEuOqY23BC5EE1ALkrlHQFtyTG28JXAhmoBakMw9AtqSY2rjLYEL0QTUgmTuEdCWHFMbbwlciCagFiRzj4C25JjaeEvgQjQBtSCZewS0JcfUxlsCF6IJqAXJ3COgLTmmNt4SuBBNQC1I5h4BbckxtfGWwIVoAmpBMvcIaEuOqY23BC5EE1ALkrlHQFtyTG28JXAhmoBakMw9AtqSY2rjLYEL0QTUgmTuEdCWHFMbbwlciCagFiRzj4C25JjaeEvgQjQBtSCZewS0JcfUxlsCF6IJqAXJ3COgLTmmNt4SuBBNQC1I5h4BbckxtfGWwIVoAmpBMvcIaEuOqY23BC5EE1ALkrlHQFtyTG28JXAhmoBakMw9AtqSY2rjLYEL0QTUgmTuEdCWHFMbbwlciCagFiRzj4C25JjaeEvgQjQBtSCZewS0JcfUxlsCF6IJqAXJ3COgLTmmNt4SuBBNQC1I5h4BbckxtfGWwIVoAmpBMvcIaEuOqY23BC5EE1ALkrk3CeiIgI7CauMtgQvRBNSCZO4R0JYcUxtvCVyIJqAWJHOPgLbkmNp4S+BCNAG1IJl7BLQlx9TGWwIXogmoBcncI6AtOaY23hK4EE1ALUjm3jSgIwIaVhtvCVyIJqAWJHOPgLbkmNp4S+BCNAG1IJl7BLQlx9TGWwIXogmoBcncI6AtOaY23hK4EE1ALUjmHgFtyTG18ZbAhWgCakEy9whoS46pjbcELkQTUAuSuUdAW3JMbbwlcCGagFqQzD0C2pJjauMtgQvRBNSCZO5N+0lAR2G18ZbAhWgCakEy9whoS46pjbcELkQTUAuSuUdAW3JMbbwlcCGagFqQzD0C2pJjauMtgQvRBNSCZO4R0JYcUxtvCVyIJqAWJHOPgLbkmNp4S+BCNAG1IJl7BLQlx9TGWwIXogmoBcncI6AtOaY23hK4EE1ALUjmHgFtyTG18ZbAhWgCakEy9whoS46pjbcELkQTUAuSuUdAW3JMbbwlcCGagFqQzD0C2pJjauMtgQvRBNSC5C4S0Dk5pjbeErgQTUAtiG+dgIbVxlsCF6IJqAXxrRPQsNp4S+BCNAG1IL51AhpWG28JXIgmoBbEt05Aw2rjLYEL0QTUgvjWCWhYbbwlcCGagFoQ3zoBDauNtwQuRBNQC+JbJ6BhtfGWwIVoAmpBfOsENKw23hK4EE1ALYhvnYCG1cZbAheiCagF8a0T0LDaeEvgQjQBtSC+dQIaVhtvCVyIJqAWxLdOQMNq4y2BC9EE1IL41gloWG28JXAhmoBaEN86AQ2rjbcELkQTUAviWyegYbXxlsCFaAJqQXzrBDSsNt4SuBAdOqB//Lujo2u/+O0qxHOhBHRCjqmNtwQuREcO6JdHk7n2mxWI40JHBHRCjqmNtwQuRAcO6MOja/8wGv3w0dFPfrcMyb/QeghoWG28JXAhOm5An3109Kv616c3p7+mkOwLnQwBDauNtwQuRMcN6NObs2ued45+uQzJvtDJENCw2nhL4EJ03IDOh4BugxxTG28JXIgmoE9vzh9F+nEz37qmqnz7DMMwW5n+A3r/6LXmPwkowzAlT+8BfchpTFshx9TGWwIXoqPfhH9449ryY/AEtA9yTG28JXAhOl5AH05Pn59m875x/ZOA9kGOqY23BC5Exw7ol2Y/CWgP5JjaeEvgQnS8gLbz7M7RT5efhDSFOC50REAn5JjaeEvgQnTkgN45+tmfbIjjQkcEdEKOqY23BC5EBw7o/XX9JKA9kGNq4y2BC9FxA/r05lEzry39FQH1k2Nq4y2BC9FxA/rwiIBukRxTG28JXIiOG9DzIL51AhpWG28JXIgmoCaEYRimxCGgDMMwmTNAQPuc5QMOMkG18Q42e+9NQHdzgmrjHWz23puA7uYE1cY72Oy9NwHdzQmqjXew2XtvArqbE1Qb72Cz994EdDcnqDbewWbvvQnobk5QbbyDzd5773xAGYZhdnUIKMMwTOYQUIZhmMwhoAzDMJlDQBmGYTKHgDIMw2QOAWUYhskcAsowDJM5ux3QZ7++cXT0i9+qD2OYeXqzeWuUBe2yPwd//Lujo2umauHefzv2/m+zN2UM5F3P9zdm70ZZgvdOB3T2vnU/Md98vri507y31IJ22Z+DL6dvqnXtN/VvAnnfn3r/dFW1bO96nn00GtxEWgAACUtJREFUezvfIrx3OqB3jn7229EPH619++SS5tmd+ZvzLWgX/Tl4eHTtH0a13eTnJo739zcm3n87/YrH8Z7M+B+PqVwR3rsc0O9vTH6unt6cXkEpeuqbdLOALmgX/TkYXxf5Vf3r+NrHryJ5j2vxy/qXqWQg73q+vzELaBneuxzQ+7Oi3J9+u5U843+V/+Y/5rqJdtGfg6c3Z7fY7qyoFu3dzNQ/lvf4H82/n94HWob3Lgf0zvT6yfiG3vIbzxc393/6z3PNBe0Yn4NJQON5Tx9NieV95+i12YNIZXjvcECffTS7Oj9/1K7smX3vLGjH+BxMbrjF8/6PG3U1Ynk/HN98n7oV4k1Ad2biBnRy+y2a952jo2v/PArmPfmnkoAOM8knde9ObsiZ1YD+5HchPgcPJ6cxBfN+9n/+642ja/89mPfkvpqVgO6z934EdM/+VcqbqNdAH964Vt//Fc57NPpjfRs+kvf9yePvXAMdZvb3k5o3QQN6f3YafTTveup7BAN5f39jYkdAB5q9fWQub2I+Cv/lUXPmXyzvyUxyEcd79vyr2TOOyvDe5YA254Tt3blhefNw6VS42flxRX8Ont2ZPZ1xFMm7eQLBNKBxvBcDWob3Lgd0b5+dkDdNQMt4hsZmcyd56l4g7zvzGxuvhfKezuxmehneuxzQ8T/UP93L58fmTRPQBe2yPwf3U61A3t/fOPqbP42efTk7/SCM93RmAS3De5cDOvphT1+hJW/md/8saJf8OZi9As/R7GUAwnjXX+vpq1BNbskH8p5M80BREd47HdDRD78ef05/sWf/JuVOe//5gnbBn4OH7X1iE/Uo3uP5IX0d1EDe9cwfaS/Be7cDyjAMs8NDQBmGYTKHgDIMw2QOAWUYhskcAsowDJM5BJRhGCZzCCjDMEzmEFCGYZjMIaAMwzCZQ0AZhmEyh4AyDMNkDgFlGIbJHALKMAyTOQQ0xhxXi3N18meX7rku9KT60e3kcs4+PayqH304/3XrMxhoLdk4gkeHl8/b3OiTfnZr8olldn8IaIwZIqATxvhPml+3PtsHffXn9mdoveqT6+cf0Gaf9EeHzi8NM9AQ0BgzQEAfHVZ/lv669dk+aN1n6BzV48mntvtFrnzY+RfD7MgQ0Ehzdqs69/ZlxzlZuPJ1WlXvpr9ufbYPWle79ap9XXV8dMiN+L0YAhppthzQgw/TX7c+2wetD+ga1fEnuKemH/f6lWK2NQQ00hDQbtM5oKe93SX76HCgzyLjGgIaaRYDOs3D5M/uvl5VB6+Of3f3jaqqXvli9hGP3zsc//n8t/OpP2r84el9oCfT+1YP3pz9+uHyen3lrMa8sPI3T65XV88+fXH8V2/fS8EvfTBKf7t0HA3ww/UXfJ7awkXPSPWBNBc+Njtp7y5euFxLdf4pvfwc8vSTvqo8ASTK40vgXtA9GAIaadYE9M9mDzH96PbHC004aR5yenX5UqYf/t75AV1cH2+91Txmvfg345r8p9env794ewE845rHkQZ05YIP/nqGXKe2dCnzA1kT0IXLXR/QR4ezW/DryfOALip/1SjOPgM1hQfi92AIaKRZE9BxP+6NzsY/5y9Ur3wzOvukmv7snkyvNn338VJBj+cfv/Ao/PLt2qX1mjP+i8cfrPzNuCbjMt0bPb41u7o3/vtLX0wAV40Lms8MtHjBFz8fjb5+ffrgznq1ZhZJSwFtb8IvXe7am/DzW/DryfOALiiPyzs+jslHXZ5fFrfh92AIaKRZF9BJNuof6suzv6g7MP6hnn3wycLP8vjPr87++LyALq/XnHebC1j4mxr87uzwZnWZXOD4t+cdRxrQ6QU3i83mWrVmlkjrArp8uWsDOr/PdD25DWiqPL83uT2+9mCYHR4CGmnWBHT+wzu/6T69Bd78LC+vzf58/CN+TkCX11vO8t/M+zS76PnJQafnHkcS0PkFNz2bRn6tWnsJC6R1AV2+3HUBbY9vPfl44d+IRvlk9epmzw/4MdsZAhpp1gR04Wd5NOtC+rHpo9HJnx+fE9CV9Tln5W/GNZlfxZzWJL2WuO440oAuC8wucp3afBZJawO6fLnrAtruryfPA7qgfFo/QvX5KJ32IpgdHgIaadY+Cj/7ffqzXt/KbKcNTfuzv5SZxaqsrM85K3+z3K3FTK47jjSg84e+m8Xpf65TW/oMpCgroCuXu1FA15DbR+EXSNMHml54+5u1B8fs5BDQSNNTQJv75nYvoCtXensK6NJ14P4DOj3VqX4Ufn4iEwHdhyGgkaZjQM374Da/Brq4ngZ08W+eG9A19wWWdA20nq/ff7FOaPNUJgK6D0NAI02XgK67D27z+0AX163OTWc1oO3JQJfPuS9wOaDWfaDPC2hKSg5k0SzrPtDuAR2N0vOsuA90L4aARpouAW0fP176WT5JqnPOo/DL6yln8W+Wa3KanO/07vrjWA3oyfzq22k1exT+/IAukeZXdpfMli93o0fhuwR09R8lHoXfkyGgkaZTQOuTu+/Nfp++RMb8vMznnge6sN5ylv9mOaDzc3zm56Oax7EaUOM80PMDukSaB/p00SzrPNBO10DnpzG1/0ZwHuheDAGNNJ0COnn+zQcj8+k7r3yx0TOR0vWEvfQ3K7dnT1eeiWQex0pA02cMXR1tEFCDVP928jLzt0fTrp19s3K55zwTqe1gp4COf3/wzvjPH99KruzyTKQ9GAIaaboFtH0O+tKdcbMnd1/8n+cGdGk9ZS/+zeodguueC794HKsBXX0u/HMCukRqnuV/6ZN5yadPJFq83LUBHZvMnwvf7T7QR4eNY3uKA3eB7sEQ0EjTMaCjx+/Vjwu3LxHUzNerr8ZkVWVhfYG98DfGIyorr8ZkHYcR0NmrJr36zRJybUCXSNNXSHpnfiBfjf/y8r3ly13/cnYLr8Zkktc9iHT26ZX6RNCGUF8Ct+D3YAgow/Q2fb4eaF+XxGxzCCjD9Db9XW/kTZH2Ywgow/Q3fV1x5ArongwBZZgep6drjlwB3ZMhoAzT4zzvfeE3G94Xfl+GgDJMn/Po0P8EorNb3IDfkyGgDMMwmUNAGYZhMoeAMgzDZA4BZRiGyRwCyjAMkzkElGEYJnMIKMMwTOYQUIZhmMwhoAzDMJlDQBmGYTKHgDIMw2QOAWUYhskcAsowDJM5BJRhGCZz/j89sZuWxNHgtwAAAABJRU5ErkJggg==" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<div class="sourceCode" id="cb71"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb71-1"><a href="#cb71-1" tabindex="-1"></a>ordered_plots1 <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb71-2"><a href="#cb71-2" tabindex="-1"></a>  plots[[<span class="dv">1</span>]], plots[[<span class="dv">2</span>]],  </span>
<span id="cb71-3"><a href="#cb71-3" tabindex="-1"></a>  plots[[<span class="dv">3</span>]]</span>
<span id="cb71-4"><a href="#cb71-4" tabindex="-1"></a>)</span>
<span id="cb71-5"><a href="#cb71-5" tabindex="-1"></a></span>
<span id="cb71-6"><a href="#cb71-6" tabindex="-1"></a></span>
<span id="cb71-7"><a href="#cb71-7" tabindex="-1"></a>ordered_plots2 <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb71-8"><a href="#cb71-8" tabindex="-1"></a>  plots[[<span class="dv">4</span>]], </span>
<span id="cb71-9"><a href="#cb71-9" tabindex="-1"></a>  plots[[<span class="dv">5</span>]], plots[[<span class="dv">6</span>]],  </span>
<span id="cb71-10"><a href="#cb71-10" tabindex="-1"></a>  plots[[<span class="dv">6</span>]], plots[[<span class="dv">7</span>]], </span>
<span id="cb71-11"><a href="#cb71-11" tabindex="-1"></a>  plots[[<span class="dv">8</span>]], plots[[<span class="dv">9</span>]]</span>
<span id="cb71-12"><a href="#cb71-12" tabindex="-1"></a>)</span></code></pre></div>
</div>
</div>
<div id="d.4-no-response-analysis" class="section level2 unnumbered">
<h2 class="unnumbered">D.4 No response analysis</h2>
<div id="table-d11-t-test-results-indicate-no-major-difference-of-non-response-rate-across-covariates" class="section level3 unnumbered">
<h3 class="unnumbered">Table D11: t-test results indicate no major
difference of non-response rate across covariates</h3>
<div class="sourceCode" id="cb72"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb72-1"><a href="#cb72-1" tabindex="-1"></a><span class="fu">source</span>(<span class="st">&quot;t_test_function.R&quot;</span>)</span>
<span id="cb72-2"><a href="#cb72-2" tabindex="-1"></a></span>
<span id="cb72-3"><a href="#cb72-3" tabindex="-1"></a>treatment_group <span class="ot">&lt;-</span> judo48p <span class="sc">|&gt;</span> <span class="fu">filter</span>(cutoff <span class="sc">==</span> <span class="dv">1</span>)</span>
<span id="cb72-4"><a href="#cb72-4" tabindex="-1"></a>control_group <span class="ot">&lt;-</span> judo48p <span class="sc">|&gt;</span> <span class="fu">filter</span>(cutoff <span class="sc">==</span> <span class="dv">0</span>)</span>
<span id="cb72-5"><a href="#cb72-5" tabindex="-1"></a></span>
<span id="cb72-6"><a href="#cb72-6" tabindex="-1"></a>calculate_no_response <span class="ot">&lt;-</span> <span class="cf">function</span>(df, var) {</span>
<span id="cb72-7"><a href="#cb72-7" tabindex="-1"></a>  no_response <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(df[[var]]), <span class="dv">1</span>, <span class="dv">0</span>)</span>
<span id="cb72-8"><a href="#cb72-8" tabindex="-1"></a>  <span class="fu">return</span>(no_response)</span>
<span id="cb72-9"><a href="#cb72-9" tabindex="-1"></a>}</span>
<span id="cb72-10"><a href="#cb72-10" tabindex="-1"></a></span>
<span id="cb72-11"><a href="#cb72-11" tabindex="-1"></a></span>
<span id="cb72-12"><a href="#cb72-12" tabindex="-1"></a>variables <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;age&quot;</span>, <span class="st">&quot;female&quot;</span>, <span class="st">&quot;education&quot;</span>, <span class="st">&quot;income&quot;</span>, <span class="st">&quot;psu_rul&quot;</span>)</span>
<span id="cb72-13"><a href="#cb72-13" tabindex="-1"></a></span>
<span id="cb72-14"><a href="#cb72-14" tabindex="-1"></a>results <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb72-15"><a href="#cb72-15" tabindex="-1"></a>  <span class="at">Variable =</span> <span class="fu">character</span>(),</span>
<span id="cb72-16"><a href="#cb72-16" tabindex="-1"></a>  <span class="at">Control_Mean =</span> <span class="fu">numeric</span>(),</span>
<span id="cb72-17"><a href="#cb72-17" tabindex="-1"></a>  <span class="at">Treatment_Mean =</span> <span class="fu">numeric</span>(),</span>
<span id="cb72-18"><a href="#cb72-18" tabindex="-1"></a>  <span class="at">T_statistic =</span> <span class="fu">numeric</span>(),</span>
<span id="cb72-19"><a href="#cb72-19" tabindex="-1"></a>  <span class="at">P_value =</span> <span class="fu">numeric</span>()</span>
<span id="cb72-20"><a href="#cb72-20" tabindex="-1"></a>)</span>
<span id="cb72-21"><a href="#cb72-21" tabindex="-1"></a></span>
<span id="cb72-22"><a href="#cb72-22" tabindex="-1"></a><span class="cf">for</span> (var <span class="cf">in</span> variables) {</span>
<span id="cb72-23"><a href="#cb72-23" tabindex="-1"></a>  t_result <span class="ot">&lt;-</span> <span class="fu">t_test_function</span>(judo48p, var)</span>
<span id="cb72-24"><a href="#cb72-24" tabindex="-1"></a>  results <span class="ot">&lt;-</span> <span class="fu">rbind</span>(results, <span class="fu">data.frame</span>(</span>
<span id="cb72-25"><a href="#cb72-25" tabindex="-1"></a>    <span class="at">Variable =</span> var, </span>
<span id="cb72-26"><a href="#cb72-26" tabindex="-1"></a>    <span class="at">Control_Mean =</span> t_result[<span class="dv">1</span>], </span>
<span id="cb72-27"><a href="#cb72-27" tabindex="-1"></a>    <span class="at">Treatment_Mean =</span> t_result[<span class="dv">2</span>], </span>
<span id="cb72-28"><a href="#cb72-28" tabindex="-1"></a>    <span class="at">T_statistic =</span> t_result[<span class="dv">3</span>], </span>
<span id="cb72-29"><a href="#cb72-29" tabindex="-1"></a>    <span class="at">P_value =</span> t_result[<span class="dv">4</span>]</span>
<span id="cb72-30"><a href="#cb72-30" tabindex="-1"></a>  ))</span>
<span id="cb72-31"><a href="#cb72-31" tabindex="-1"></a>}</span></code></pre></div>
<table class=" lightable-classic" style="font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="4">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
<em>The first gold medal case</em>
</div>
</th>
</tr>
<tr>
<th style="text-align:left;">
Variable
</th>
<th style="text-align:right;">
Control
</th>
<th style="text-align:right;">
Treatment
</th>
<th style="text-align:right;">
t-statistics
</th>
<th style="text-align:right;">
p-value
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;font-weight: bold;">
Age
</td>
<td style="text-align:right;">
0.09
</td>
<td style="text-align:right;">
0.12
</td>
<td style="text-align:right;">
1.71
</td>
<td style="text-align:right;">
0.09
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
Female
</td>
<td style="text-align:right;">
0.09
</td>
<td style="text-align:right;">
0.12
</td>
<td style="text-align:right;">
1.74
</td>
<td style="text-align:right;">
0.08
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
Education
</td>
<td style="text-align:right;">
0.09
</td>
<td style="text-align:right;">
0.12
</td>
<td style="text-align:right;">
1.80
</td>
<td style="text-align:right;">
0.07
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
Income
</td>
<td style="text-align:right;">
0.21
</td>
<td style="text-align:right;">
0.24
</td>
<td style="text-align:right;">
1.07
</td>
<td style="text-align:right;">
0.28
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
In-partisans
</td>
<td style="text-align:right;">
0.10
</td>
<td style="text-align:right;">
0.14
</td>
<td style="text-align:right;">
1.62
</td>
<td style="text-align:right;">
0.11
</td>
</tr>
</tbody>
</table>
<div class="sourceCode" id="cb73"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb73-1"><a href="#cb73-1" tabindex="-1"></a>treatment_group <span class="ot">&lt;-</span> judo66p <span class="sc">|&gt;</span> <span class="fu">filter</span>(cutoff <span class="sc">==</span> <span class="dv">1</span>)</span>
<span id="cb73-2"><a href="#cb73-2" tabindex="-1"></a>control_group <span class="ot">&lt;-</span> judo66p <span class="sc">|&gt;</span> <span class="fu">filter</span>(cutoff <span class="sc">==</span> <span class="dv">0</span>)</span>
<span id="cb73-3"><a href="#cb73-3" tabindex="-1"></a></span>
<span id="cb73-4"><a href="#cb73-4" tabindex="-1"></a>calculate_no_response <span class="ot">&lt;-</span> <span class="cf">function</span>(df, var) {</span>
<span id="cb73-5"><a href="#cb73-5" tabindex="-1"></a>  no_response <span class="ot">&lt;-</span> <span class="fu">ifelse</span>(<span class="fu">is.na</span>(df[[var]]), <span class="dv">1</span>, <span class="dv">0</span>)</span>
<span id="cb73-6"><a href="#cb73-6" tabindex="-1"></a>  <span class="fu">return</span>(no_response)</span>
<span id="cb73-7"><a href="#cb73-7" tabindex="-1"></a>}</span>
<span id="cb73-8"><a href="#cb73-8" tabindex="-1"></a></span>
<span id="cb73-9"><a href="#cb73-9" tabindex="-1"></a></span>
<span id="cb73-10"><a href="#cb73-10" tabindex="-1"></a>variables <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;age&quot;</span>, <span class="st">&quot;female&quot;</span>, <span class="st">&quot;education&quot;</span>, <span class="st">&quot;income&quot;</span>, <span class="st">&quot;psu_rul&quot;</span>)</span>
<span id="cb73-11"><a href="#cb73-11" tabindex="-1"></a></span>
<span id="cb73-12"><a href="#cb73-12" tabindex="-1"></a>results <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb73-13"><a href="#cb73-13" tabindex="-1"></a>  <span class="at">Variable =</span> <span class="fu">character</span>(),</span>
<span id="cb73-14"><a href="#cb73-14" tabindex="-1"></a>  <span class="at">Control_Mean =</span> <span class="fu">numeric</span>(),</span>
<span id="cb73-15"><a href="#cb73-15" tabindex="-1"></a>  <span class="at">Treatment_Mean =</span> <span class="fu">numeric</span>(),</span>
<span id="cb73-16"><a href="#cb73-16" tabindex="-1"></a>  <span class="at">T_statistic =</span> <span class="fu">numeric</span>(),</span>
<span id="cb73-17"><a href="#cb73-17" tabindex="-1"></a>  <span class="at">P_value =</span> <span class="fu">numeric</span>()</span>
<span id="cb73-18"><a href="#cb73-18" tabindex="-1"></a>)</span>
<span id="cb73-19"><a href="#cb73-19" tabindex="-1"></a></span>
<span id="cb73-20"><a href="#cb73-20" tabindex="-1"></a><span class="cf">for</span> (var <span class="cf">in</span> variables) {</span>
<span id="cb73-21"><a href="#cb73-21" tabindex="-1"></a>  t_result <span class="ot">&lt;-</span> <span class="fu">t_test_function</span>(judo66p, var)</span>
<span id="cb73-22"><a href="#cb73-22" tabindex="-1"></a>  results <span class="ot">&lt;-</span> <span class="fu">rbind</span>(results, <span class="fu">data.frame</span>(</span>
<span id="cb73-23"><a href="#cb73-23" tabindex="-1"></a>    <span class="at">Variable =</span> var, </span>
<span id="cb73-24"><a href="#cb73-24" tabindex="-1"></a>    <span class="at">Control_Mean =</span> t_result[<span class="dv">1</span>], </span>
<span id="cb73-25"><a href="#cb73-25" tabindex="-1"></a>    <span class="at">Treatment_Mean =</span> t_result[<span class="dv">2</span>], </span>
<span id="cb73-26"><a href="#cb73-26" tabindex="-1"></a>    <span class="at">T_statistic =</span> t_result[<span class="dv">3</span>], </span>
<span id="cb73-27"><a href="#cb73-27" tabindex="-1"></a>    <span class="at">P_value =</span> t_result[<span class="dv">4</span>]</span>
<span id="cb73-28"><a href="#cb73-28" tabindex="-1"></a>  ))</span>
<span id="cb73-29"><a href="#cb73-29" tabindex="-1"></a>}</span></code></pre></div>
<table class=" lightable-classic" style="font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
</caption>
<thead>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="4">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
<em>The second gold medal case</em>
</div>
</th>
</tr>
<tr>
<th style="text-align:left;">
Variable
</th>
<th style="text-align:right;">
Control
</th>
<th style="text-align:right;">
Treatment
</th>
<th style="text-align:right;">
t-statistics
</th>
<th style="text-align:right;">
p-value
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;font-weight: bold;">
Age
</td>
<td style="text-align:right;">
0.05
</td>
<td style="text-align:right;">
0.04
</td>
<td style="text-align:right;">
-0.62
</td>
<td style="text-align:right;">
0.53
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
Female
</td>
<td style="text-align:right;">
0.06
</td>
<td style="text-align:right;">
0.05
</td>
<td style="text-align:right;">
-0.73
</td>
<td style="text-align:right;">
0.46
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
Education
</td>
<td style="text-align:right;">
0.05
</td>
<td style="text-align:right;">
0.04
</td>
<td style="text-align:right;">
-0.62
</td>
<td style="text-align:right;">
0.53
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
Income
</td>
<td style="text-align:right;">
0.18
</td>
<td style="text-align:right;">
0.19
</td>
<td style="text-align:right;">
0.33
</td>
<td style="text-align:right;">
0.74
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
In-partisans
</td>
<td style="text-align:right;">
0.10
</td>
<td style="text-align:right;">
0.12
</td>
<td style="text-align:right;">
1.01
</td>
<td style="text-align:right;">
0.31
</td>
</tr>
</tbody>
</table>
</div>
<div id="d.5-time-trends" class="section level3 unnumbered">
<h3 class="unnumbered">D.5 Time trends</h3>
</div>
<div id="table-d12-the-falsification-test-using-the-placebo-treatment-the-median-cutoff-point-of-the-control-group-also-detects-a-null-effect-on-cabinet-approval-the-first-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D12: The falsification test using the
placebo treatment (the median cutoff point of the control group) also
detects a null effect on cabinet approval (the first gold medal
case)</h3>
</div>
<div id="table-d13-the-falsification-test-using-the-placebo-treatment-the-median-cutoff-point-of-the-control-group-also-detects-a-null-effect-on-feeling-thermometer-the-first-gold-medal-case-the-first-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D13: The falsification test using the
placebo treatment (the median cutoff point of the control group) also
detects a null effect on feeling thermometer (the first gold medal case)
(the first gold medal case)</h3>
<div class="sourceCode" id="cb74"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb74-1"><a href="#cb74-1" tabindex="-1"></a>df_rdd48_left <span class="ot">&lt;-</span> df_rdd48 <span class="sc">|&gt;</span> dplyr<span class="sc">::</span><span class="fu">filter</span>(running_var <span class="sc">&lt;</span> <span class="dv">0</span>)</span>
<span id="cb74-2"><a href="#cb74-2" tabindex="-1"></a>median_point  <span class="ot">&lt;-</span> <span class="fu">median</span>(df_rdd48_left<span class="sc">$</span>running_var, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb74-3"><a href="#cb74-3" tabindex="-1"></a>df_rdd48_left <span class="ot">&lt;-</span> df_rdd48_left <span class="sc">|&gt;</span></span>
<span id="cb74-4"><a href="#cb74-4" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb74-5"><a href="#cb74-5" tabindex="-1"></a>    <span class="at">placebo_treatment =</span> <span class="fu">as.integer</span>(running_var <span class="sc">&gt;=</span> median_point),</span>
<span id="cb74-6"><a href="#cb74-6" tabindex="-1"></a>    <span class="at">running_var =</span> running_var <span class="sc">-</span> median_point</span>
<span id="cb74-7"><a href="#cb74-7" tabindex="-1"></a>  )</span>
<span id="cb74-8"><a href="#cb74-8" tabindex="-1"></a></span>
<span id="cb74-9"><a href="#cb74-9" tabindex="-1"></a>fit_set <span class="ot">&lt;-</span> <span class="cf">function</span>(y){</span>
<span id="cb74-10"><a href="#cb74-10" tabindex="-1"></a>  f1 <span class="ot">&lt;-</span> <span class="fu">reformulate</span>(<span class="fu">c</span>(<span class="st">&quot;placebo_treatment&quot;</span>,<span class="st">&quot;running_var&quot;</span>,</span>
<span id="cb74-11"><a href="#cb74-11" tabindex="-1"></a>                      <span class="st">&quot;placebo_treatment:running_var&quot;</span>,<span class="st">&quot;I(running_var^2)&quot;</span>), <span class="at">response =</span> y)</span>
<span id="cb74-12"><a href="#cb74-12" tabindex="-1"></a>  f2 <span class="ot">&lt;-</span> <span class="fu">reformulate</span>(<span class="fu">c</span>(<span class="st">&quot;placebo_treatment&quot;</span>,<span class="st">&quot;running_var&quot;</span>,</span>
<span id="cb74-13"><a href="#cb74-13" tabindex="-1"></a>                      <span class="st">&quot;placebo_treatment:running_var&quot;</span>), <span class="at">response =</span> y)</span>
<span id="cb74-14"><a href="#cb74-14" tabindex="-1"></a>  f3 <span class="ot">&lt;-</span> <span class="fu">reformulate</span>(<span class="fu">c</span>(<span class="st">&quot;running_var&quot;</span>,<span class="st">&quot;I(running_var^2)&quot;</span>), <span class="at">response =</span> y)</span>
<span id="cb74-15"><a href="#cb74-15" tabindex="-1"></a>  f4 <span class="ot">&lt;-</span> <span class="fu">reformulate</span>(<span class="fu">c</span>(<span class="st">&quot;running_var&quot;</span>,<span class="st">&quot;placebo_treatment&quot;</span>,<span class="st">&quot;I(running_var^2)&quot;</span>), <span class="at">response =</span> y)</span>
<span id="cb74-16"><a href="#cb74-16" tabindex="-1"></a>  <span class="fu">list</span>(</span>
<span id="cb74-17"><a href="#cb74-17" tabindex="-1"></a>    <span class="at">m1 =</span> <span class="fu">lm</span>(f1, <span class="at">data =</span> df_rdd48_left),</span>
<span id="cb74-18"><a href="#cb74-18" tabindex="-1"></a>    <span class="at">m2 =</span> <span class="fu">lm</span>(f2, <span class="at">data =</span> df_rdd48_left),</span>
<span id="cb74-19"><a href="#cb74-19" tabindex="-1"></a>    <span class="at">m3 =</span> <span class="fu">lm</span>(f3, <span class="at">data =</span> df_rdd48_left),</span>
<span id="cb74-20"><a href="#cb74-20" tabindex="-1"></a>    <span class="at">m4 =</span> <span class="fu">lm</span>(f4, <span class="at">data =</span> df_rdd48_left)</span>
<span id="cb74-21"><a href="#cb74-21" tabindex="-1"></a>  )</span>
<span id="cb74-22"><a href="#cb74-22" tabindex="-1"></a>}</span>
<span id="cb74-23"><a href="#cb74-23" tabindex="-1"></a>mods_cab <span class="ot">&lt;-</span> <span class="fu">fit_set</span>(<span class="st">&quot;cabap&quot;</span>)</span>
<span id="cb74-24"><a href="#cb74-24" tabindex="-1"></a>mods_ftj <span class="ot">&lt;-</span> <span class="fu">fit_set</span>(<span class="st">&quot;ftj&quot;</span>)</span>
<span id="cb74-25"><a href="#cb74-25" tabindex="-1"></a></span>
<span id="cb74-26"><a href="#cb74-26" tabindex="-1"></a>star_p <span class="ot">&lt;-</span> <span class="cf">function</span>(p){</span>
<span id="cb74-27"><a href="#cb74-27" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">is.na</span>(p)) <span class="st">&quot;&quot;</span> <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.001</span>) <span class="st">&quot;***&quot;</span> <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.01</span>) <span class="st">&quot;**&quot;</span> <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.05</span>) <span class="st">&quot;*&quot;</span> <span class="cf">else</span> <span class="st">&quot;&quot;</span></span>
<span id="cb74-28"><a href="#cb74-28" tabindex="-1"></a>}</span>
<span id="cb74-29"><a href="#cb74-29" tabindex="-1"></a>coef_cell <span class="ot">&lt;-</span> <span class="cf">function</span>(est, se, p, <span class="at">for_latex =</span> knitr<span class="sc">::</span><span class="fu">is_latex_output</span>()){</span>
<span id="cb74-30"><a href="#cb74-30" tabindex="-1"></a>  est_txt <span class="ot">&lt;-</span> <span class="fu">sprintf</span>(<span class="st">&quot;%.3f%s&quot;</span>, est, <span class="fu">star_p</span>(p))</span>
<span id="cb74-31"><a href="#cb74-31" tabindex="-1"></a>  se_txt  <span class="ot">&lt;-</span> <span class="fu">sprintf</span>(<span class="st">&quot;(%.3f)&quot;</span>, se)</span>
<span id="cb74-32"><a href="#cb74-32" tabindex="-1"></a>  <span class="cf">if</span> (for_latex) <span class="fu">paste0</span>(est_txt, <span class="st">&quot; </span><span class="sc">\\\\</span><span class="st"> </span><span class="sc">\n</span><span class="st">&quot;</span>, se_txt) <span class="cf">else</span> <span class="fu">paste0</span>(est_txt, <span class="st">&quot;&lt;br&gt;&quot;</span>, se_txt)</span>
<span id="cb74-33"><a href="#cb74-33" tabindex="-1"></a>}</span>
<span id="cb74-34"><a href="#cb74-34" tabindex="-1"></a></span>
<span id="cb74-35"><a href="#cb74-35" tabindex="-1"></a>var_order  <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;placebo_treatment&quot;</span>,</span>
<span id="cb74-36"><a href="#cb74-36" tabindex="-1"></a>                <span class="st">&quot;running_var&quot;</span>,</span>
<span id="cb74-37"><a href="#cb74-37" tabindex="-1"></a>                <span class="st">&quot;I(running_var^2)&quot;</span>,</span>
<span id="cb74-38"><a href="#cb74-38" tabindex="-1"></a>                <span class="st">&quot;placebo_treatment:running_var&quot;</span>,</span>
<span id="cb74-39"><a href="#cb74-39" tabindex="-1"></a>                <span class="st">&quot;(Intercept)&quot;</span>)</span>
<span id="cb74-40"><a href="#cb74-40" tabindex="-1"></a>var_labels <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Placebo treatment&quot;</span>,</span>
<span id="cb74-41"><a href="#cb74-41" tabindex="-1"></a>                <span class="st">&quot;Times&quot;</span>,</span>
<span id="cb74-42"><a href="#cb74-42" tabindex="-1"></a>                <span class="st">&quot;Times^2&quot;</span>,</span>
<span id="cb74-43"><a href="#cb74-43" tabindex="-1"></a>                <span class="st">&quot;Placebo treatment×Times&quot;</span>,<span class="st">&quot;Constant&quot;</span>)</span>
<span id="cb74-44"><a href="#cb74-44" tabindex="-1"></a></span>
<span id="cb74-45"><a href="#cb74-45" tabindex="-1"></a>f_value <span class="ot">&lt;-</span> <span class="cf">function</span>(m) <span class="fu">unname</span>(<span class="fu">summary</span>(m)<span class="sc">$</span>fstatistic[<span class="st">&quot;value&quot;</span>])</span>
<span id="cb74-46"><a href="#cb74-46" tabindex="-1"></a>f_numdf <span class="ot">&lt;-</span> <span class="cf">function</span>(m) <span class="fu">unname</span>(<span class="fu">summary</span>(m)<span class="sc">$</span>fstatistic[<span class="st">&quot;numdf&quot;</span>])</span>
<span id="cb74-47"><a href="#cb74-47" tabindex="-1"></a>f_dendf <span class="ot">&lt;-</span> <span class="cf">function</span>(m) <span class="fu">unname</span>(<span class="fu">summary</span>(m)<span class="sc">$</span>fstatistic[<span class="st">&quot;dendf&quot;</span>])</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="4">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Dependent variable: Cabinet approval
</div>
</th>
</tr>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
Model 1
</th>
<th style="text-align:center;font-weight: bold;">
Model 2
</th>
<th style="text-align:center;font-weight: bold;">
Model 3
</th>
<th style="text-align:center;font-weight: bold;">
Model 4
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Placebo treatment
</td>
<td style="text-align:center;">
-0.001<br>(0.075)
</td>
<td style="text-align:center;">
-0.007<br>(0.074)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
0.022<br>(0.073)
</td>
</tr>
<tr>
<td style="text-align:left;">
Times
</td>
<td style="text-align:center;">
0.023<br>(0.019)
</td>
<td style="text-align:center;">
0.016<br>(0.018)
</td>
<td style="text-align:center;">
0.000<br>(0.005)
</td>
<td style="text-align:center;">
-0.002<br>(0.010)
</td>
</tr>
<tr>
<td style="text-align:left;">
Times^2
</td>
<td style="text-align:center;">
0.001<br>(0.001)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
-0.001<br>(0.001)
</td>
<td style="text-align:center;">
-0.001<br>(0.001)
</td>
</tr>
<tr>
<td style="text-align:left;">
Placebo treatment×Times
</td>
<td style="text-align:center;">
-0.044<br>(0.028)
</td>
<td style="text-align:center;">
-0.028<br>(0.020)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
</tr>
<tr>
<td style="text-align:left;">
Constant
</td>
<td style="text-align:center;">
0.287***<br>(0.064)
</td>
<td style="text-align:center;">
0.278***<br>(0.064)
</td>
<td style="text-align:center;">
0.242***<br>(0.020)
</td>
<td style="text-align:center;">
0.228***<br>(0.047)
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
558
</td>
<td style="text-align:center;">
558
</td>
<td style="text-align:center;">
558
</td>
<td style="text-align:center;">
558
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.006
</td>
<td style="text-align:center;">
0.005
</td>
<td style="text-align:center;">
0.001
</td>
<td style="text-align:center;">
0.002
</td>
</tr>
<tr>
<td style="text-align:left;">
Adjusted <span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
-0.002
</td>
<td style="text-align:center;">
-0.001
</td>
<td style="text-align:center;">
-0.002
</td>
<td style="text-align:center;">
-0.004
</td>
</tr>
<tr>
<td style="text-align:left;">
Residual Std. Error
</td>
<td style="text-align:center;">
0.423
</td>
<td style="text-align:center;">
0.423
</td>
<td style="text-align:center;">
0.424
</td>
<td style="text-align:center;">
0.424
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 553)
</td>
<td style="text-align:center;">
(df = 554)
</td>
<td style="text-align:center;">
(df = 555)
</td>
<td style="text-align:center;">
(df = 554)
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(F\)</span> Statistic
</td>
<td style="text-align:center;">
0.775
</td>
<td style="text-align:center;">
0.851
</td>
<td style="text-align:center;">
0.382
</td>
<td style="text-align:center;">
0.290
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 4; 553)
</td>
<td style="text-align:center;">
(df = 3; 554)
</td>
<td style="text-align:center;">
(df = 2; 555)
</td>
<td style="text-align:center;">
(df = 3; 554)
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Standard errors in parentheses.
</td>
</tr>
</tfoot>
</table>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="4">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Dependent variable: Feeling thermometer
</div>
</th>
</tr>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
Model 1
</th>
<th style="text-align:center;font-weight: bold;">
Model 2
</th>
<th style="text-align:center;font-weight: bold;">
Model 3
</th>
<th style="text-align:center;font-weight: bold;">
Model 4
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Placebo treatment
</td>
<td style="text-align:center;">
0.209<br>(4.499)
</td>
<td style="text-align:center;">
0.431<br>(4.458)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
0.506<br>(4.311)
</td>
</tr>
<tr>
<td style="text-align:left;">
Times
</td>
<td style="text-align:center;">
0.096<br>(1.166)
</td>
<td style="text-align:center;">
0.349<br>(1.113)
</td>
<td style="text-align:center;">
-0.158<br>(0.330)
</td>
<td style="text-align:center;">
-0.219<br>(0.611)
</td>
</tr>
<tr>
<td style="text-align:left;">
Times^2
</td>
<td style="text-align:center;">
-0.037<br>(0.048)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
-0.058<br>(0.033)
</td>
<td style="text-align:center;">
-0.055<br>(0.041)
</td>
</tr>
<tr>
<td style="text-align:left;">
Placebo treatment×Times
</td>
<td style="text-align:center;">
-0.556<br>(1.618)
</td>
<td style="text-align:center;">
-1.184<br>(1.234)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
</tr>
<tr>
<td style="text-align:left;">
Constant
</td>
<td style="text-align:center;">
41.445***<br>(3.950)
</td>
<td style="text-align:center;">
41.779***<br>(3.919)
</td>
<td style="text-align:center;">
41.015***<br>(1.212)
</td>
<td style="text-align:center;">
40.709***<br>(2.939)
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
558
</td>
<td style="text-align:center;">
558
</td>
<td style="text-align:center;">
558
</td>
<td style="text-align:center;">
558
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.005
</td>
<td style="text-align:center;">
0.005
</td>
<td style="text-align:center;">
0.005
</td>
<td style="text-align:center;">
0.005
</td>
</tr>
<tr>
<td style="text-align:left;">
Adjusted <span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
-0.002
</td>
<td style="text-align:center;">
-0.001
</td>
<td style="text-align:center;">
0.001
</td>
<td style="text-align:center;">
-0.001
</td>
</tr>
<tr>
<td style="text-align:left;">
Residual Std. Error
</td>
<td style="text-align:center;">
26.624
</td>
<td style="text-align:center;">
26.605
</td>
<td style="text-align:center;">
26.578
</td>
<td style="text-align:center;">
26.602
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 553)
</td>
<td style="text-align:center;">
(df = 554)
</td>
<td style="text-align:center;">
(df = 555)
</td>
<td style="text-align:center;">
(df = 554)
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(F\)</span> Statistic
</td>
<td style="text-align:center;">
0.682
</td>
<td style="text-align:center;">
0.844
</td>
<td style="text-align:center;">
1.316
</td>
<td style="text-align:center;">
0.881
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 4; 553)
</td>
<td style="text-align:center;">
(df = 3; 554)
</td>
<td style="text-align:center;">
(df = 2; 555)
</td>
<td style="text-align:center;">
(df = 3; 554)
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Standard errors in parentheses.
</td>
</tr>
</tfoot>
</table>
</div>
<div id="table-d14-the-falsification-test-using-the-placebo-treatment-the-median-cutoff-point-of-the-control-group-also-detects-a-null-effect-on-cabinet-approval-the-second-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D14: The falsification test using the
placebo treatment (the median cutoff point of the control group) also
detects a null effect on cabinet approval (the second gold medal
case)</h3>
</div>
<div id="table-d15-the-falsification-test-using-the-placebo-treatment-the-median-cutoff-point-of-the-control-group-also-detects-a-null-effect-on-feeling-thermometer-the-second-gold-medal-case" class="section level3 unnumbered">
<h3 class="unnumbered">Table D15: The falsification test using the
placebo treatment (the median cutoff point of the control group) also
detects a null effect on feeling thermometer (the second gold medal
case)</h3>
<div class="sourceCode" id="cb75"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb75-1"><a href="#cb75-1" tabindex="-1"></a>df_rdd66_left <span class="ot">&lt;-</span> df_rdd66 <span class="sc">|&gt;</span> dplyr<span class="sc">::</span><span class="fu">filter</span>(running_var <span class="sc">&lt;</span> <span class="dv">0</span>)</span>
<span id="cb75-2"><a href="#cb75-2" tabindex="-1"></a>median_point  <span class="ot">&lt;-</span> <span class="fu">median</span>(df_rdd66_left<span class="sc">$</span>running_var, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb75-3"><a href="#cb75-3" tabindex="-1"></a>df_rdd66_left <span class="ot">&lt;-</span> df_rdd66_left <span class="sc">|&gt;</span></span>
<span id="cb75-4"><a href="#cb75-4" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">mutate</span>(</span>
<span id="cb75-5"><a href="#cb75-5" tabindex="-1"></a>    <span class="at">placebo_treatment =</span> <span class="fu">as.integer</span>(running_var <span class="sc">&gt;=</span> median_point),</span>
<span id="cb75-6"><a href="#cb75-6" tabindex="-1"></a>    <span class="at">running_var =</span> running_var <span class="sc">-</span> median_point</span>
<span id="cb75-7"><a href="#cb75-7" tabindex="-1"></a>  )</span>
<span id="cb75-8"><a href="#cb75-8" tabindex="-1"></a></span>
<span id="cb75-9"><a href="#cb75-9" tabindex="-1"></a>fit_set <span class="ot">&lt;-</span> <span class="cf">function</span>(y){</span>
<span id="cb75-10"><a href="#cb75-10" tabindex="-1"></a>  f1 <span class="ot">&lt;-</span> <span class="fu">reformulate</span>(<span class="fu">c</span>(<span class="st">&quot;placebo_treatment&quot;</span>,<span class="st">&quot;running_var&quot;</span>,</span>
<span id="cb75-11"><a href="#cb75-11" tabindex="-1"></a>                      <span class="st">&quot;placebo_treatment:running_var&quot;</span>,<span class="st">&quot;I(running_var^2)&quot;</span>), <span class="at">response =</span> y)</span>
<span id="cb75-12"><a href="#cb75-12" tabindex="-1"></a>  f2 <span class="ot">&lt;-</span> <span class="fu">reformulate</span>(<span class="fu">c</span>(<span class="st">&quot;placebo_treatment&quot;</span>,<span class="st">&quot;running_var&quot;</span>,</span>
<span id="cb75-13"><a href="#cb75-13" tabindex="-1"></a>                      <span class="st">&quot;placebo_treatment:running_var&quot;</span>), <span class="at">response =</span> y)</span>
<span id="cb75-14"><a href="#cb75-14" tabindex="-1"></a>  f3 <span class="ot">&lt;-</span> <span class="fu">reformulate</span>(<span class="fu">c</span>(<span class="st">&quot;running_var&quot;</span>,<span class="st">&quot;I(running_var^2)&quot;</span>), <span class="at">response =</span> y)</span>
<span id="cb75-15"><a href="#cb75-15" tabindex="-1"></a>  f4 <span class="ot">&lt;-</span> <span class="fu">reformulate</span>(<span class="fu">c</span>(<span class="st">&quot;running_var&quot;</span>,<span class="st">&quot;placebo_treatment&quot;</span>,<span class="st">&quot;I(running_var^2)&quot;</span>), <span class="at">response =</span> y)</span>
<span id="cb75-16"><a href="#cb75-16" tabindex="-1"></a>  <span class="fu">list</span>(</span>
<span id="cb75-17"><a href="#cb75-17" tabindex="-1"></a>    <span class="at">m1 =</span> <span class="fu">lm</span>(f1, <span class="at">data =</span> df_rdd66_left),</span>
<span id="cb75-18"><a href="#cb75-18" tabindex="-1"></a>    <span class="at">m2 =</span> <span class="fu">lm</span>(f2, <span class="at">data =</span> df_rdd66_left),</span>
<span id="cb75-19"><a href="#cb75-19" tabindex="-1"></a>    <span class="at">m3 =</span> <span class="fu">lm</span>(f3, <span class="at">data =</span> df_rdd66_left),</span>
<span id="cb75-20"><a href="#cb75-20" tabindex="-1"></a>    <span class="at">m4 =</span> <span class="fu">lm</span>(f4, <span class="at">data =</span> df_rdd66_left)</span>
<span id="cb75-21"><a href="#cb75-21" tabindex="-1"></a>  )</span>
<span id="cb75-22"><a href="#cb75-22" tabindex="-1"></a>}</span>
<span id="cb75-23"><a href="#cb75-23" tabindex="-1"></a>mods_cab <span class="ot">&lt;-</span> <span class="fu">fit_set</span>(<span class="st">&quot;cabap&quot;</span>)</span>
<span id="cb75-24"><a href="#cb75-24" tabindex="-1"></a>mods_ftj <span class="ot">&lt;-</span> <span class="fu">fit_set</span>(<span class="st">&quot;ftj&quot;</span>)</span>
<span id="cb75-25"><a href="#cb75-25" tabindex="-1"></a></span>
<span id="cb75-26"><a href="#cb75-26" tabindex="-1"></a>star_p <span class="ot">&lt;-</span> <span class="cf">function</span>(p){</span>
<span id="cb75-27"><a href="#cb75-27" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">is.na</span>(p)) <span class="st">&quot;&quot;</span> <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.001</span>) <span class="st">&quot;***&quot;</span> <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.01</span>) <span class="st">&quot;**&quot;</span> <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.05</span>) <span class="st">&quot;*&quot;</span> <span class="cf">else</span> <span class="st">&quot;&quot;</span></span>
<span id="cb75-28"><a href="#cb75-28" tabindex="-1"></a>}</span>
<span id="cb75-29"><a href="#cb75-29" tabindex="-1"></a>coef_cell <span class="ot">&lt;-</span> <span class="cf">function</span>(est, se, p, <span class="at">for_latex =</span> knitr<span class="sc">::</span><span class="fu">is_latex_output</span>()){</span>
<span id="cb75-30"><a href="#cb75-30" tabindex="-1"></a>  est_txt <span class="ot">&lt;-</span> <span class="fu">sprintf</span>(<span class="st">&quot;%.3f%s&quot;</span>, est, <span class="fu">star_p</span>(p))</span>
<span id="cb75-31"><a href="#cb75-31" tabindex="-1"></a>  se_txt  <span class="ot">&lt;-</span> <span class="fu">sprintf</span>(<span class="st">&quot;(%.3f)&quot;</span>, se)</span>
<span id="cb75-32"><a href="#cb75-32" tabindex="-1"></a>  <span class="cf">if</span> (for_latex) <span class="fu">paste0</span>(est_txt, <span class="st">&quot; </span><span class="sc">\\\\</span><span class="st"> </span><span class="sc">\n</span><span class="st">&quot;</span>, se_txt) <span class="cf">else</span> <span class="fu">paste0</span>(est_txt, <span class="st">&quot;&lt;br&gt;&quot;</span>, se_txt)</span>
<span id="cb75-33"><a href="#cb75-33" tabindex="-1"></a>}</span>
<span id="cb75-34"><a href="#cb75-34" tabindex="-1"></a></span>
<span id="cb75-35"><a href="#cb75-35" tabindex="-1"></a>var_order  <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;placebo_treatment&quot;</span>,</span>
<span id="cb75-36"><a href="#cb75-36" tabindex="-1"></a>                <span class="st">&quot;running_var&quot;</span>,</span>
<span id="cb75-37"><a href="#cb75-37" tabindex="-1"></a>                <span class="st">&quot;I(running_var^2)&quot;</span>,</span>
<span id="cb75-38"><a href="#cb75-38" tabindex="-1"></a>                <span class="st">&quot;placebo_treatment:running_var&quot;</span>,<span class="st">&quot;(Intercept)&quot;</span>)</span>
<span id="cb75-39"><a href="#cb75-39" tabindex="-1"></a>var_labels <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;Placebo treatment&quot;</span>,</span>
<span id="cb75-40"><a href="#cb75-40" tabindex="-1"></a>                <span class="st">&quot;Times&quot;</span>,</span>
<span id="cb75-41"><a href="#cb75-41" tabindex="-1"></a>                <span class="st">&quot;Times^2&quot;</span>,</span>
<span id="cb75-42"><a href="#cb75-42" tabindex="-1"></a>                <span class="st">&quot;Placebo treatment×Times&quot;</span>,<span class="st">&quot;Constant&quot;</span>)</span>
<span id="cb75-43"><a href="#cb75-43" tabindex="-1"></a></span>
<span id="cb75-44"><a href="#cb75-44" tabindex="-1"></a>f_value <span class="ot">&lt;-</span> <span class="cf">function</span>(m) <span class="fu">unname</span>(<span class="fu">summary</span>(m)<span class="sc">$</span>fstatistic[<span class="st">&quot;value&quot;</span>])</span>
<span id="cb75-45"><a href="#cb75-45" tabindex="-1"></a>f_numdf <span class="ot">&lt;-</span> <span class="cf">function</span>(m) <span class="fu">unname</span>(<span class="fu">summary</span>(m)<span class="sc">$</span>fstatistic[<span class="st">&quot;numdf&quot;</span>])</span>
<span id="cb75-46"><a href="#cb75-46" tabindex="-1"></a>f_dendf <span class="ot">&lt;-</span> <span class="cf">function</span>(m) <span class="fu">unname</span>(<span class="fu">summary</span>(m)<span class="sc">$</span>fstatistic[<span class="st">&quot;dendf&quot;</span>])</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
Model 1
</th>
<th style="text-align:center;font-weight: bold;">
Model 2
</th>
<th style="text-align:center;font-weight: bold;">
Model 3
</th>
<th style="text-align:center;font-weight: bold;">
Model 4
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Placebo treatment
</td>
<td style="text-align:center;">
-0.121<br>(0.147)
</td>
<td style="text-align:center;">
0.028<br>(0.106)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
0.068<br>(0.102)
</td>
</tr>
<tr>
<td style="text-align:left;">
Times
</td>
<td style="text-align:center;">
0.193<br>(0.128)
</td>
<td style="text-align:center;">
0.020<br>(0.035)
</td>
<td style="text-align:center;">
-0.011<br>(0.012)
</td>
<td style="text-align:center;">
-0.024<br>(0.022)
</td>
</tr>
<tr>
<td style="text-align:left;">
Times^2
</td>
<td style="text-align:center;">
0.031<br>(0.023)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
-0.006<br>(0.005)
</td>
<td style="text-align:center;">
-0.006<br>(0.005)
</td>
</tr>
<tr>
<td style="text-align:left;">
Placebo treatment×Times
</td>
<td style="text-align:center;">
-0.335<br>(0.201)
</td>
<td style="text-align:center;">
-0.070<br>(0.045)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
</tr>
<tr>
<td style="text-align:left;">
Constant
</td>
<td style="text-align:center;">
0.408**<br>(0.155)
</td>
<td style="text-align:center;">
0.234**<br>(0.089)
</td>
<td style="text-align:center;">
0.220***<br>(0.037)
</td>
<td style="text-align:center;">
0.179*<br>(0.072)
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
226
</td>
<td style="text-align:center;">
226
</td>
<td style="text-align:center;">
226
</td>
<td style="text-align:center;">
226
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.024
</td>
<td style="text-align:center;">
0.015
</td>
<td style="text-align:center;">
0.009
</td>
<td style="text-align:center;">
0.011
</td>
</tr>
<tr>
<td style="text-align:left;">
Adjusted <span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.006
</td>
<td style="text-align:center;">
0.002
</td>
<td style="text-align:center;">
-0.000
</td>
<td style="text-align:center;">
-0.002
</td>
</tr>
<tr>
<td style="text-align:left;">
Residual Std. Error
</td>
<td style="text-align:center;">
0.392
</td>
<td style="text-align:center;">
0.393
</td>
<td style="text-align:center;">
0.393
</td>
<td style="text-align:center;">
0.394
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 221)
</td>
<td style="text-align:center;">
(df = 222)
</td>
<td style="text-align:center;">
(df = 223)
</td>
<td style="text-align:center;">
(df = 222)
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(F\)</span> Statistic
</td>
<td style="text-align:center;">
1.361
</td>
<td style="text-align:center;">
1.160
</td>
<td style="text-align:center;">
0.966
</td>
<td style="text-align:center;">
0.819
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 4; 221)
</td>
<td style="text-align:center;">
(df = 3; 222)
</td>
<td style="text-align:center;">
(df = 2; 223)
</td>
<td style="text-align:center;">
(df = 3; 222)
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Standard errors in parentheses.
</td>
</tr>
</tfoot>
</table>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
Model 1
</th>
<th style="text-align:center;font-weight: bold;">
Model 2
</th>
<th style="text-align:center;font-weight: bold;">
Model 3
</th>
<th style="text-align:center;font-weight: bold;">
Model 4
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Placebo treatment
</td>
<td style="text-align:center;">
-11.546<br>(9.439)
</td>
<td style="text-align:center;">
4.494<br>(5.961)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
3.583<br>(5.711)
</td>
</tr>
<tr>
<td style="text-align:left;">
Times
</td>
<td style="text-align:center;">
16.103<br>(8.183)
</td>
<td style="text-align:center;">
-2.412<br>(1.972)
</td>
<td style="text-align:center;">
-0.537<br>(0.777)
</td>
<td style="text-align:center;">
-1.211<br>(1.299)
</td>
</tr>
<tr>
<td style="text-align:left;">
Times^2
</td>
<td style="text-align:center;">
3.360*<br>(1.457)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
0.353<br>(0.305)
</td>
<td style="text-align:center;">
0.356<br>(0.304)
</td>
</tr>
<tr>
<td style="text-align:left;">
Placebo treatment×Times
</td>
<td style="text-align:center;">
-26.793*<br>(12.407)
</td>
<td style="text-align:center;">
1.690<br>(2.603)
</td>
<td style="text-align:center;">
</td>
<td style="text-align:center;">
</td>
</tr>
<tr>
<td style="text-align:left;">
Constant
</td>
<td style="text-align:center;">
53.675***<br>(9.501)
</td>
<td style="text-align:center;">
34.951***<br>(5.005)
</td>
<td style="text-align:center;">
37.482***<br>(2.191)
</td>
<td style="text-align:center;">
35.328***<br>(4.065)
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
226
</td>
<td style="text-align:center;">
226
</td>
<td style="text-align:center;">
226
</td>
<td style="text-align:center;">
226
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.033
</td>
<td style="text-align:center;">
0.008
</td>
<td style="text-align:center;">
0.010
</td>
<td style="text-align:center;">
0.012
</td>
</tr>
<tr>
<td style="text-align:left;">
Adjusted <span class="math inline">\(R^2\)</span>
</td>
<td style="text-align:center;">
0.016
</td>
<td style="text-align:center;">
-0.006
</td>
<td style="text-align:center;">
0.001
</td>
<td style="text-align:center;">
-0.001
</td>
</tr>
<tr>
<td style="text-align:left;">
Residual Std. Error
</td>
<td style="text-align:center;">
24.463
</td>
<td style="text-align:center;">
24.725
</td>
<td style="text-align:center;">
24.641
</td>
<td style="text-align:center;">
24.675
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 221)
</td>
<td style="text-align:center;">
(df = 222)
</td>
<td style="text-align:center;">
(df = 223)
</td>
<td style="text-align:center;">
(df = 222)
</td>
</tr>
<tr>
<td style="text-align:left;">
<span class="math inline">\(F\)</span> Statistic
</td>
<td style="text-align:center;">
1.896
</td>
<td style="text-align:center;">
0.587
</td>
<td style="text-align:center;">
1.152
</td>
<td style="text-align:center;">
0.891
</td>
</tr>
<tr>
<td style="text-align:left;">
</td>
<td style="text-align:center;">
(df = 4; 221)
</td>
<td style="text-align:center;">
(df = 3; 222)
</td>
<td style="text-align:center;">
(df = 2; 223)
</td>
<td style="text-align:center;">
(df = 3; 222)
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ✱ Signif. codes: * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01.
Standard errors in parentheses.
</td>
</tr>
</tfoot>
</table>
</div>
<div id="table-d16-dickey-fuller-test-and-fractional-integration-d-value-suggest-both-before-and-after-series-are-stationary-and-have-no-trends" class="section level3 unnumbered">
<h3 class="unnumbered">Table D16: Dickey-Fuller Test and Fractional
Integration d-value suggest both before and after series are stationary
and have no trends</h3>
<div class="sourceCode" id="cb76"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb76-1"><a href="#cb76-1" tabindex="-1"></a>df_before_cutoff <span class="ot">&lt;-</span> df_rdd48 <span class="sc">|&gt;</span></span>
<span id="cb76-2"><a href="#cb76-2" tabindex="-1"></a>  <span class="fu">filter</span>(StartDate <span class="sc">&lt;</span> cutoff_time)</span>
<span id="cb76-3"><a href="#cb76-3" tabindex="-1"></a></span>
<span id="cb76-4"><a href="#cb76-4" tabindex="-1"></a>df_after_cutoff <span class="ot">&lt;-</span> df_rdd48 <span class="sc">|&gt;</span></span>
<span id="cb76-5"><a href="#cb76-5" tabindex="-1"></a>  <span class="fu">filter</span>(StartDate <span class="sc">&gt;=</span> cutoff_time)</span>
<span id="cb76-6"><a href="#cb76-6" tabindex="-1"></a></span>
<span id="cb76-7"><a href="#cb76-7" tabindex="-1"></a></span>
<span id="cb76-8"><a href="#cb76-8" tabindex="-1"></a>cabap_before <span class="ot">&lt;-</span> df_before_cutoff<span class="sc">$</span>cabap</span>
<span id="cb76-9"><a href="#cb76-9" tabindex="-1"></a>ftj_before <span class="ot">&lt;-</span> df_before_cutoff<span class="sc">$</span>ftj</span>
<span id="cb76-10"><a href="#cb76-10" tabindex="-1"></a></span>
<span id="cb76-11"><a href="#cb76-11" tabindex="-1"></a></span>
<span id="cb76-12"><a href="#cb76-12" tabindex="-1"></a>adf_cabap_before <span class="ot">&lt;-</span> <span class="fu">adf.test</span>(cabap_before)</span>
<span id="cb76-13"><a href="#cb76-13" tabindex="-1"></a>adf_ftj_before <span class="ot">&lt;-</span> <span class="fu">adf.test</span>(ftj_before)</span>
<span id="cb76-14"><a href="#cb76-14" tabindex="-1"></a></span>
<span id="cb76-15"><a href="#cb76-15" tabindex="-1"></a></span>
<span id="cb76-16"><a href="#cb76-16" tabindex="-1"></a>fdgph_cabap_before <span class="ot">&lt;-</span> <span class="fu">fdGPH</span>(cabap_before)</span>
<span id="cb76-17"><a href="#cb76-17" tabindex="-1"></a>fdgph_ftj_before <span class="ot">&lt;-</span> <span class="fu">fdGPH</span>(ftj_before)</span>
<span id="cb76-18"><a href="#cb76-18" tabindex="-1"></a></span>
<span id="cb76-19"><a href="#cb76-19" tabindex="-1"></a>dickey_fuller_cabap <span class="ot">&lt;-</span> adf_cabap_before<span class="sc">$</span>statistic</span>
<span id="cb76-20"><a href="#cb76-20" tabindex="-1"></a>dickey_fuller_ftj <span class="ot">&lt;-</span> adf_ftj_before<span class="sc">$</span>statistic</span>
<span id="cb76-21"><a href="#cb76-21" tabindex="-1"></a>lag_order_cabap <span class="ot">&lt;-</span> adf_cabap_before<span class="sc">$</span>parameter</span>
<span id="cb76-22"><a href="#cb76-22" tabindex="-1"></a>lag_order_ftj <span class="ot">&lt;-</span> adf_ftj_before<span class="sc">$</span>parameter</span>
<span id="cb76-23"><a href="#cb76-23" tabindex="-1"></a>p_value_cabap <span class="ot">&lt;-</span> adf_cabap_before<span class="sc">$</span>p.value</span>
<span id="cb76-24"><a href="#cb76-24" tabindex="-1"></a>p_value_ftj <span class="ot">&lt;-</span> adf_ftj_before<span class="sc">$</span>p.value</span>
<span id="cb76-25"><a href="#cb76-25" tabindex="-1"></a></span>
<span id="cb76-26"><a href="#cb76-26" tabindex="-1"></a>d_cabap <span class="ot">&lt;-</span> fdgph_cabap_before<span class="sc">$</span>d</span>
<span id="cb76-27"><a href="#cb76-27" tabindex="-1"></a>sd_as_cabap <span class="ot">&lt;-</span> fdgph_cabap_before<span class="sc">$</span>sd.as</span>
<span id="cb76-28"><a href="#cb76-28" tabindex="-1"></a>sd_reg_cabap <span class="ot">&lt;-</span> fdgph_cabap_before<span class="sc">$</span>sd.reg</span>
<span id="cb76-29"><a href="#cb76-29" tabindex="-1"></a></span>
<span id="cb76-30"><a href="#cb76-30" tabindex="-1"></a>d_ftj <span class="ot">&lt;-</span> fdgph_ftj_before<span class="sc">$</span>d</span>
<span id="cb76-31"><a href="#cb76-31" tabindex="-1"></a>sd_as_ftj <span class="ot">&lt;-</span> fdgph_ftj_before<span class="sc">$</span>sd.as</span>
<span id="cb76-32"><a href="#cb76-32" tabindex="-1"></a>sd_reg_ftj <span class="ot">&lt;-</span> fdgph_ftj_before<span class="sc">$</span>sd.reg</span>
<span id="cb76-33"><a href="#cb76-33" tabindex="-1"></a></span>
<span id="cb76-34"><a href="#cb76-34" tabindex="-1"></a></span>
<span id="cb76-35"><a href="#cb76-35" tabindex="-1"></a>results_df <span class="ot">&lt;-</span> <span class="fu">data.frame</span>(</span>
<span id="cb76-36"><a href="#cb76-36" tabindex="-1"></a>  <span class="at">Test =</span> <span class="fu">c</span>(<span class="st">&quot;ADF Test (cabap)&quot;</span>, <span class="st">&quot;ADF Test (ftj)&quot;</span>, <span class="st">&quot;fdGPH (cabap)&quot;</span>, <span class="st">&quot;fdGPH (ftj)&quot;</span>),</span>
<span id="cb76-37"><a href="#cb76-37" tabindex="-1"></a>  <span class="st">`</span><span class="at">Dickey-Fuller / d</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">c</span>(dickey_fuller_cabap, dickey_fuller_ftj, d_cabap, d_ftj),</span>
<span id="cb76-38"><a href="#cb76-38" tabindex="-1"></a>  <span class="st">`</span><span class="at">Lag Order / sd.as</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">c</span>(lag_order_cabap, lag_order_ftj, sd_as_cabap, sd_as_ftj),</span>
<span id="cb76-39"><a href="#cb76-39" tabindex="-1"></a>  <span class="st">`</span><span class="at">p-value / sd.reg</span><span class="st">`</span> <span class="ot">=</span> <span class="fu">c</span>(p_value_cabap, p_value_ftj, sd_reg_cabap, sd_reg_ftj)</span>
<span id="cb76-40"><a href="#cb76-40" tabindex="-1"></a>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
Test
</th>
<th style="text-align:center;font-weight: bold;">
Dickey-Fuller &amp; FI d-value
</th>
<th style="text-align:center;font-weight: bold;">
Lag.Order / sd.as
</th>
<th style="text-align:center;font-weight: bold;">
p-value (ADF) / sd.reg (FI)
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
ADF Test (cabap)
</td>
<td style="text-align:center;">
-8.382
</td>
<td style="text-align:center;">
8.000
</td>
<td style="text-align:center;">
0.010
</td>
</tr>
<tr>
<td style="text-align:left;">
ADF Test (ftj)
</td>
<td style="text-align:center;">
-8.529
</td>
<td style="text-align:center;">
8.000
</td>
<td style="text-align:center;">
0.010
</td>
</tr>
<tr>
<td style="text-align:left;">
fdGPH (cabap)
</td>
<td style="text-align:center;">
-0.057
</td>
<td style="text-align:center;">
0.166
</td>
<td style="text-align:center;">
0.176
</td>
</tr>
<tr>
<td style="text-align:left;">
fdGPH (ftj)
</td>
<td style="text-align:center;">
-0.263
</td>
<td style="text-align:center;">
0.166
</td>
<td style="text-align:center;">
0.189
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ADF: null is a unit root (non-stationary). FI (GPH): series
is typically treated as stationary when d &lt; 0.5.
</td>
</tr>
</tfoot>
</table>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<caption>
</caption>
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
Test
</th>
<th style="text-align:center;font-weight: bold;">
Dickey-Fuller &amp; FI d-value
</th>
<th style="text-align:center;font-weight: bold;">
Lag.Order / sd.as
</th>
<th style="text-align:center;font-weight: bold;">
p-value (ADF) / sd.reg (FI)
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
ADF Test (cabap)
</td>
<td style="text-align:center;">
-6.045
</td>
<td style="text-align:center;">
6.000
</td>
<td style="text-align:center;">
0.010
</td>
</tr>
<tr>
<td style="text-align:left;">
ADF Test (ftj)
</td>
<td style="text-align:center;">
-4.433
</td>
<td style="text-align:center;">
6.000
</td>
<td style="text-align:center;">
0.010
</td>
</tr>
<tr>
<td style="text-align:left;">
fdGPH (cabap)
</td>
<td style="text-align:center;">
0.027
</td>
<td style="text-align:center;">
0.220
</td>
<td style="text-align:center;">
0.183
</td>
</tr>
<tr>
<td style="text-align:left;">
fdGPH (ftj)
</td>
<td style="text-align:center;">
0.101
</td>
<td style="text-align:center;">
0.220
</td>
<td style="text-align:center;">
0.215
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> ADF: null is a unit root (non-stationary). FI (GPH): series
is typically treated as stationary when d &lt; 0.5.
</td>
</tr>
</tfoot>
</table>
</div>
</div>
<div id="further-supplementary-analyses-examination-of-various-placebo-effects" class="section level2 unnumbered">
<h2 class="unnumbered">Further supplementary analyses: Examination of
various placebo effects</h2>
</div>
<div id="using-opening-ceremony-data-setting-variables" class="section level2 unnumbered">
<h2 class="unnumbered">Using opening ceremony data: Setting
variables</h2>
<div class="sourceCode" id="cb77"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb77-1"><a href="#cb77-1" tabindex="-1"></a>opening <span class="ot">&lt;-</span> <span class="fu">read.csv</span>(<span class="st">&quot;opening.csv&quot;</span>) <span class="sc">|&gt;</span></span>
<span id="cb77-2"><a href="#cb77-2" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb77-3"><a href="#cb77-3" tabindex="-1"></a>    <span class="at">RecordedDate =</span> <span class="fu">as.POSIXct</span>(RecordedDate, <span class="at">format =</span> <span class="st">&quot;%Y/%m/%d %H:%M&quot;</span>, <span class="at">tz =</span> <span class="st">&quot;Asia/Tokyo&quot;</span>)</span>
<span id="cb77-4"><a href="#cb77-4" tabindex="-1"></a>  )</span>
<span id="cb77-5"><a href="#cb77-5" tabindex="-1"></a></span>
<span id="cb77-6"><a href="#cb77-6" tabindex="-1"></a>cutoff_time <span class="ot">&lt;-</span> <span class="fu">as.POSIXct</span>(<span class="st">&quot;2024/07/26 11:30&quot;</span>, <span class="at">format =</span> <span class="st">&quot;%Y/%m/%d %H:%M&quot;</span>, <span class="at">tz =</span> <span class="st">&quot;Asia/Tokyo&quot;</span>)</span>
<span id="cb77-7"><a href="#cb77-7" tabindex="-1"></a></span>
<span id="cb77-8"><a href="#cb77-8" tabindex="-1"></a>opening_proc <span class="ot">&lt;-</span> opening <span class="sc">|&gt;</span></span>
<span id="cb77-9"><a href="#cb77-9" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb77-10"><a href="#cb77-10" tabindex="-1"></a>    <span class="at">cutoff      =</span> <span class="fu">as.integer</span>(RecordedDate <span class="sc">&gt;=</span> cutoff_time),</span>
<span id="cb77-11"><a href="#cb77-11" tabindex="-1"></a>    <span class="at">running_var =</span> <span class="fu">as.numeric</span>(<span class="fu">difftime</span>(RecordedDate, cutoff_time, <span class="at">units =</span> <span class="st">&quot;mins&quot;</span>)),</span>
<span id="cb77-12"><a href="#cb77-12" tabindex="-1"></a>    <span class="at">running_var2 =</span> running_var <span class="sc">+</span> <span class="fu">rnorm</span>(<span class="fu">n</span>(), <span class="at">mean =</span> <span class="dv">0</span>, <span class="at">sd =</span> <span class="fl">0.01</span>),</span>
<span id="cb77-13"><a href="#cb77-13" tabindex="-1"></a>    </span>
<span id="cb77-14"><a href="#cb77-14" tabindex="-1"></a>    <span class="at">exposure =</span> <span class="fu">case_when</span>(</span>
<span id="cb77-15"><a href="#cb77-15" tabindex="-1"></a>      Q5 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">2</span>) <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb77-16"><a href="#cb77-16" tabindex="-1"></a>      Q5 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">3</span>, <span class="dv">4</span>) <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb77-17"><a href="#cb77-17" tabindex="-1"></a>      <span class="cn">TRUE</span>            <span class="sc">~</span> <span class="cn">NA_real_</span></span>
<span id="cb77-18"><a href="#cb77-18" tabindex="-1"></a>    ),</span>
<span id="cb77-19"><a href="#cb77-19" tabindex="-1"></a>    </span>
<span id="cb77-20"><a href="#cb77-20" tabindex="-1"></a>    </span>
<span id="cb77-21"><a href="#cb77-21" tabindex="-1"></a>    <span class="at">cabap =</span> <span class="fu">case_when</span>(</span>
<span id="cb77-22"><a href="#cb77-22" tabindex="-1"></a>      Q1 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb77-23"><a href="#cb77-23" tabindex="-1"></a>      Q1 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb77-24"><a href="#cb77-24" tabindex="-1"></a>      Q1 <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="cn">NA_real_</span>,</span>
<span id="cb77-25"><a href="#cb77-25" tabindex="-1"></a>      <span class="cn">TRUE</span>    <span class="sc">~</span> <span class="cn">NA_real_</span></span>
<span id="cb77-26"><a href="#cb77-26" tabindex="-1"></a>    ),</span>
<span id="cb77-27"><a href="#cb77-27" tabindex="-1"></a>    <span class="at">ftj =</span> Q3_11,</span>
<span id="cb77-28"><a href="#cb77-28" tabindex="-1"></a>    </span>
<span id="cb77-29"><a href="#cb77-29" tabindex="-1"></a>    </span>
<span id="cb77-30"><a href="#cb77-30" tabindex="-1"></a>    <span class="at">busi =</span> <span class="fu">case_when</span>(</span>
<span id="cb77-31"><a href="#cb77-31" tabindex="-1"></a>      Q7_1 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">5</span>,</span>
<span id="cb77-32"><a href="#cb77-32" tabindex="-1"></a>      Q7_1 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">4</span>,</span>
<span id="cb77-33"><a href="#cb77-33" tabindex="-1"></a>      Q7_1 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="dv">2</span>,</span>
<span id="cb77-34"><a href="#cb77-34" tabindex="-1"></a>      Q7_1 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb77-35"><a href="#cb77-35" tabindex="-1"></a>      <span class="cn">TRUE</span>      <span class="sc">~</span> <span class="dv">3</span></span>
<span id="cb77-36"><a href="#cb77-36" tabindex="-1"></a>    ),</span>
<span id="cb77-37"><a href="#cb77-37" tabindex="-1"></a>    <span class="at">liv =</span> <span class="fu">case_when</span>(</span>
<span id="cb77-38"><a href="#cb77-38" tabindex="-1"></a>      Q7_2 <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">5</span>,</span>
<span id="cb77-39"><a href="#cb77-39" tabindex="-1"></a>      Q7_2 <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">4</span>,</span>
<span id="cb77-40"><a href="#cb77-40" tabindex="-1"></a>      Q7_2 <span class="sc">==</span> <span class="dv">4</span> <span class="sc">~</span> <span class="dv">2</span>,</span>
<span id="cb77-41"><a href="#cb77-41" tabindex="-1"></a>      Q7_2 <span class="sc">==</span> <span class="dv">5</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb77-42"><a href="#cb77-42" tabindex="-1"></a>      <span class="cn">TRUE</span>      <span class="sc">~</span> <span class="dv">3</span></span>
<span id="cb77-43"><a href="#cb77-43" tabindex="-1"></a>    ),</span>
<span id="cb77-44"><a href="#cb77-44" tabindex="-1"></a>    </span>
<span id="cb77-45"><a href="#cb77-45" tabindex="-1"></a>    <span class="at">psu_rul =</span> <span class="fu">case_when</span>(</span>
<span id="cb77-46"><a href="#cb77-46" tabindex="-1"></a>      Q2 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">14</span>, <span class="dv">15</span>) <span class="sc">~</span> <span class="cn">NA_real_</span>,</span>
<span id="cb77-47"><a href="#cb77-47" tabindex="-1"></a>      Q2 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">1</span>, <span class="dv">4</span>)   <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb77-48"><a href="#cb77-48" tabindex="-1"></a>      <span class="cn">TRUE</span>              <span class="sc">~</span> <span class="dv">0</span></span>
<span id="cb77-49"><a href="#cb77-49" tabindex="-1"></a>    ),</span>
<span id="cb77-50"><a href="#cb77-50" tabindex="-1"></a>    </span>
<span id="cb77-51"><a href="#cb77-51" tabindex="-1"></a>    <span class="at">age =</span> Age,</span>
<span id="cb77-52"><a href="#cb77-52" tabindex="-1"></a>    </span>
<span id="cb77-53"><a href="#cb77-53" tabindex="-1"></a>    <span class="at">female =</span> <span class="fu">case_when</span>(      </span>
<span id="cb77-54"><a href="#cb77-54" tabindex="-1"></a>      Gender <span class="sc">==</span> <span class="dv">2</span> <span class="sc">~</span> <span class="dv">1</span>,</span>
<span id="cb77-55"><a href="#cb77-55" tabindex="-1"></a>      Gender <span class="sc">==</span> <span class="dv">1</span> <span class="sc">~</span> <span class="dv">0</span>,</span>
<span id="cb77-56"><a href="#cb77-56" tabindex="-1"></a>      Gender <span class="sc">==</span> <span class="dv">3</span> <span class="sc">~</span> <span class="cn">NA_real_</span>,</span>
<span id="cb77-57"><a href="#cb77-57" tabindex="-1"></a>      <span class="cn">TRUE</span>        <span class="sc">~</span> <span class="cn">NA_real_</span></span>
<span id="cb77-58"><a href="#cb77-58" tabindex="-1"></a>    ),</span>
<span id="cb77-59"><a href="#cb77-59" tabindex="-1"></a>    </span>
<span id="cb77-60"><a href="#cb77-60" tabindex="-1"></a>    <span class="at">education =</span> <span class="fu">na_if</span>(Q9, <span class="dv">5</span>),  </span>
<span id="cb77-61"><a href="#cb77-61" tabindex="-1"></a>    </span>
<span id="cb77-62"><a href="#cb77-62" tabindex="-1"></a>    <span class="at">income =</span> <span class="fu">case_when</span>(        </span>
<span id="cb77-63"><a href="#cb77-63" tabindex="-1"></a>      Q10 <span class="sc">%in%</span> <span class="fu">c</span>(<span class="dv">15</span>, <span class="dv">16</span>) <span class="sc">~</span> <span class="cn">NA_real_</span>,</span>
<span id="cb77-64"><a href="#cb77-64" tabindex="-1"></a>      <span class="cn">TRUE</span>               <span class="sc">~</span> <span class="fu">as.numeric</span>(Q10)</span>
<span id="cb77-65"><a href="#cb77-65" tabindex="-1"></a>    )</span>
<span id="cb77-66"><a href="#cb77-66" tabindex="-1"></a>  )</span>
<span id="cb77-67"><a href="#cb77-67" tabindex="-1"></a></span>
<span id="cb77-68"><a href="#cb77-68" tabindex="-1"></a></span>
<span id="cb77-69"><a href="#cb77-69" tabindex="-1"></a>df_rddop <span class="ot">&lt;-</span> opening_proc <span class="sc">|&gt;</span></span>
<span id="cb77-70"><a href="#cb77-70" tabindex="-1"></a>  dplyr<span class="sc">::</span><span class="fu">select</span>(StartDate,</span>
<span id="cb77-71"><a href="#cb77-71" tabindex="-1"></a>                RecordedDate, cutoff, running_var, running_var2,</span>
<span id="cb77-72"><a href="#cb77-72" tabindex="-1"></a>                cabap, ftj, busi, liv,</span>
<span id="cb77-73"><a href="#cb77-73" tabindex="-1"></a>                exposure, psu_rul,</span>
<span id="cb77-74"><a href="#cb77-74" tabindex="-1"></a>                age, female, education, income</span>
<span id="cb77-75"><a href="#cb77-75" tabindex="-1"></a>  )</span>
<span id="cb77-76"><a href="#cb77-76" tabindex="-1"></a></span>
<span id="cb77-77"><a href="#cb77-77" tabindex="-1"></a><span class="fu">dim</span>(df_rddop)</span></code></pre></div>
<pre><code>## [1] 843  15</code></pre>
<div id="cumlative-density" class="section level3 unnumbered">
<h3 class="unnumbered">Cumlative density</h3>
<div class="sourceCode" id="cb79"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb79-1"><a href="#cb79-1" tabindex="-1"></a>data_cum <span class="ot">&lt;-</span> opening_proc <span class="sc">|&gt;</span></span>
<span id="cb79-2"><a href="#cb79-2" tabindex="-1"></a>  <span class="fu">arrange</span>(RecordedDate) <span class="sc">|&gt;</span></span>
<span id="cb79-3"><a href="#cb79-3" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">CumulativeCount =</span> <span class="fu">row_number</span>())</span>
<span id="cb79-4"><a href="#cb79-4" tabindex="-1"></a></span>
<span id="cb79-5"><a href="#cb79-5" tabindex="-1"></a>vline_time <span class="ot">&lt;-</span> cutoff_time </span>
<span id="cb79-6"><a href="#cb79-6" tabindex="-1"></a></span>
<span id="cb79-7"><a href="#cb79-7" tabindex="-1"></a>p_cum <span class="ot">&lt;-</span> <span class="fu">ggplot</span>(data_cum, <span class="fu">aes</span>(<span class="at">x =</span> RecordedDate, <span class="at">y =</span> CumulativeCount)) <span class="sc">+</span></span>
<span id="cb79-8"><a href="#cb79-8" tabindex="-1"></a>  <span class="fu">geom_line</span>() <span class="sc">+</span></span>
<span id="cb79-9"><a href="#cb79-9" tabindex="-1"></a>  <span class="fu">geom_point</span>() <span class="sc">+</span></span>
<span id="cb79-10"><a href="#cb79-10" tabindex="-1"></a>  <span class="fu">geom_vline</span>(<span class="at">xintercept =</span> <span class="fu">as.numeric</span>(vline_time), <span class="at">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="at">color =</span> <span class="st">&quot;red&quot;</span>) <span class="sc">+</span></span>
<span id="cb79-11"><a href="#cb79-11" tabindex="-1"></a>  <span class="fu">annotate</span>(<span class="st">&quot;text&quot;</span>,</span>
<span id="cb79-12"><a href="#cb79-12" tabindex="-1"></a>           <span class="at">x =</span> vline_time,</span>
<span id="cb79-13"><a href="#cb79-13" tabindex="-1"></a>           <span class="at">y =</span> <span class="fu">max</span>(data_cum<span class="sc">$</span>CumulativeCount),</span>
<span id="cb79-14"><a href="#cb79-14" tabindex="-1"></a>           <span class="at">label =</span> <span class="st">&quot;ceremony</span><span class="sc">\n</span><span class="st">start&quot;</span>,</span>
<span id="cb79-15"><a href="#cb79-15" tabindex="-1"></a>           <span class="at">angle =</span> <span class="dv">90</span>, <span class="at">vjust =</span> <span class="sc">-</span><span class="fl">0.5</span>, <span class="at">hjust =</span> <span class="fl">1.1</span>, <span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb79-16"><a href="#cb79-16" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">title =</span> <span class="st">&quot;Cumulative Data Collection Over Time&quot;</span>,</span>
<span id="cb79-17"><a href="#cb79-17" tabindex="-1"></a>       <span class="at">x =</span> <span class="st">&quot;Recorded Date&quot;</span>,</span>
<span id="cb79-18"><a href="#cb79-18" tabindex="-1"></a>       <span class="at">y =</span> <span class="st">&quot;Cumulative Count&quot;</span>) <span class="sc">+</span></span>
<span id="cb79-19"><a href="#cb79-19" tabindex="-1"></a>  <span class="fu">theme_igray</span>() <span class="sc">+</span></span>
<span id="cb79-20"><a href="#cb79-20" tabindex="-1"></a>  <span class="fu">scale_x_datetime</span>(<span class="at">date_labels =</span> <span class="st">&quot;%m/%d %H:%M&quot;</span>)</span>
<span id="cb79-21"><a href="#cb79-21" tabindex="-1"></a></span>
<span id="cb79-22"><a href="#cb79-22" tabindex="-1"></a><span class="fu">print</span>(p_cum)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb80"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb80-1"><a href="#cb80-1" tabindex="-1"></a>milestones <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="dv">50</span>, <span class="dv">100</span>, <span class="dv">150</span>, <span class="dv">200</span>, <span class="dv">250</span>, <span class="dv">300</span>)</span>
<span id="cb80-2"><a href="#cb80-2" tabindex="-1"></a></span>
<span id="cb80-3"><a href="#cb80-3" tabindex="-1"></a>milestone_df <span class="ot">&lt;-</span> data_cum <span class="sc">|&gt;</span></span>
<span id="cb80-4"><a href="#cb80-4" tabindex="-1"></a>  <span class="fu">filter</span>(CumulativeCount <span class="sc">%in%</span> milestones) <span class="sc">|&gt;</span></span>
<span id="cb80-5"><a href="#cb80-5" tabindex="-1"></a>  <span class="fu">transmute</span>(</span>
<span id="cb80-6"><a href="#cb80-6" tabindex="-1"></a>    <span class="at">Milestone       =</span> CumulativeCount,</span>
<span id="cb80-7"><a href="#cb80-7" tabindex="-1"></a>    <span class="at">TimeReached     =</span> RecordedDate</span>
<span id="cb80-8"><a href="#cb80-8" tabindex="-1"></a>  ) <span class="sc">|&gt;</span></span>
<span id="cb80-9"><a href="#cb80-9" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb80-10"><a href="#cb80-10" tabindex="-1"></a>    <span class="at">MinutesFromStart =</span> <span class="fu">as.numeric</span>(<span class="fu">difftime</span>(TimeReached, <span class="fu">first</span>(data_cum<span class="sc">$</span>RecordedDate), <span class="at">units =</span> <span class="st">&quot;mins&quot;</span>))</span>
<span id="cb80-11"><a href="#cb80-11" tabindex="-1"></a>  )</span>
<span id="cb80-12"><a href="#cb80-12" tabindex="-1"></a></span>
<span id="cb80-13"><a href="#cb80-13" tabindex="-1"></a><span class="fu">print</span>(milestone_df)</span></code></pre></div>
<pre><code>##   Milestone         TimeReached MinutesFromStart
## 1        50 2024-07-26 07:07:00               93
## 2       100 2024-07-26 08:51:00              197
## 3       150 2024-07-26 12:39:00              425
## 4       200 2024-07-26 15:32:00              598
## 5       250 2024-07-26 16:20:00              646
## 6       300 2024-07-26 16:55:00              681</code></pre>
</div>
<div id="balance-check" class="section level3 unnumbered">
<h3 class="unnumbered">Balance check</h3>
<div class="sourceCode" id="cb82"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb82-1"><a href="#cb82-1" tabindex="-1"></a>df_balance <span class="ot">&lt;-</span> df_rddop <span class="sc">|&gt;</span></span>
<span id="cb82-2"><a href="#cb82-2" tabindex="-1"></a>  <span class="fu">transmute</span>(</span>
<span id="cb82-3"><a href="#cb82-3" tabindex="-1"></a>    <span class="at">cutoff    =</span> cutoff,    </span>
<span id="cb82-4"><a href="#cb82-4" tabindex="-1"></a>    <span class="at">Age       =</span> age,      </span>
<span id="cb82-5"><a href="#cb82-5" tabindex="-1"></a>    <span class="at">Female    =</span> female,     </span>
<span id="cb82-6"><a href="#cb82-6" tabindex="-1"></a>    <span class="at">Education =</span> education,       </span>
<span id="cb82-7"><a href="#cb82-7" tabindex="-1"></a>    <span class="at">Income    =</span> income,       </span>
<span id="cb82-8"><a href="#cb82-8" tabindex="-1"></a>    <span class="at">Inpartisans   =</span> psu_rul    </span>
<span id="cb82-9"><a href="#cb82-9" tabindex="-1"></a>  ) <span class="sc">|&gt;</span></span>
<span id="cb82-10"><a href="#cb82-10" tabindex="-1"></a>  <span class="fu">drop_na</span>()</span>
<span id="cb82-11"><a href="#cb82-11" tabindex="-1"></a></span>
<span id="cb82-12"><a href="#cb82-12" tabindex="-1"></a>df_balance <span class="ot">&lt;-</span> df_balance <span class="sc">|&gt;</span></span>
<span id="cb82-13"><a href="#cb82-13" tabindex="-1"></a>  <span class="fu">mutate</span>(</span>
<span id="cb82-14"><a href="#cb82-14" tabindex="-1"></a>    <span class="at">cutoff =</span> <span class="fu">factor</span>(cutoff, <span class="at">levels =</span> <span class="fu">c</span>(<span class="dv">0</span>, <span class="dv">1</span>), <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;Before&quot;</span>, <span class="st">&quot;After&quot;</span>))</span>
<span id="cb82-15"><a href="#cb82-15" tabindex="-1"></a>  )</span>
<span id="cb82-16"><a href="#cb82-16" tabindex="-1"></a></span>
<span id="cb82-17"><a href="#cb82-17" tabindex="-1"></a>vtable<span class="sc">::</span><span class="fu">sumtable</span>(</span>
<span id="cb82-18"><a href="#cb82-18" tabindex="-1"></a>  df_balance,</span>
<span id="cb82-19"><a href="#cb82-19" tabindex="-1"></a>  <span class="at">group =</span> <span class="st">&quot;cutoff&quot;</span>,     </span>
<span id="cb82-20"><a href="#cb82-20" tabindex="-1"></a>  <span class="at">group.test =</span> <span class="cn">TRUE</span>     </span>
<span id="cb82-21"><a href="#cb82-21" tabindex="-1"></a>)</span></code></pre></div>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>
Summary Statistics
</caption>
<thead>
<tr>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="1">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
cutoff
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Before
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
After
</div>
</th>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
</tr>
<tr>
<th style="text-align:left;">
Variable
</th>
<th style="text-align:left;">
N
</th>
<th style="text-align:left;">
Mean
</th>
<th style="text-align:left;">
SD
</th>
<th style="text-align:left;">
N
</th>
<th style="text-align:left;">
Mean
</th>
<th style="text-align:left;">
SD
</th>
<th style="text-align:left;">
Test
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Age
</td>
<td style="text-align:left;">
92
</td>
<td style="text-align:left;">
49
</td>
<td style="text-align:left;">
15
</td>
<td style="text-align:left;">
490
</td>
<td style="text-align:left;">
52
</td>
<td style="text-align:left;">
15
</td>
<td style="text-align:left;">
F=2.682<sup></sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
Female
</td>
<td style="text-align:left;">
92
</td>
<td style="text-align:left;">
0.4
</td>
<td style="text-align:left;">
0.49
</td>
<td style="text-align:left;">
490
</td>
<td style="text-align:left;">
0.42
</td>
<td style="text-align:left;">
0.49
</td>
<td style="text-align:left;">
F=0.158<sup></sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
Education
</td>
<td style="text-align:left;">
92
</td>
<td style="text-align:left;">
3.4
</td>
<td style="text-align:left;">
0.83
</td>
<td style="text-align:left;">
490
</td>
<td style="text-align:left;">
3.2
</td>
<td style="text-align:left;">
0.92
</td>
<td style="text-align:left;">
F=5.901<sup>**</sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
Income
</td>
<td style="text-align:left;">
92
</td>
<td style="text-align:left;">
7.1
</td>
<td style="text-align:left;">
3.4
</td>
<td style="text-align:left;">
490
</td>
<td style="text-align:left;">
7
</td>
<td style="text-align:left;">
3.2
</td>
<td style="text-align:left;">
F=0.089<sup></sup>
</td>
</tr>
<tr>
<td style="text-align:left;">
Inpartisans
</td>
<td style="text-align:left;">
92
</td>
<td style="text-align:left;">
0.35
</td>
<td style="text-align:left;">
0.48
</td>
<td style="text-align:left;">
490
</td>
<td style="text-align:left;">
0.29
</td>
<td style="text-align:left;">
0.45
</td>
<td style="text-align:left;">
F=1.434<sup></sup>
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; border:0;" colspan="100%">
<sup></sup> Statistical significance markers: * p&lt;0.1; ** p&lt;0.05;
*** p&lt;0.01
</td>
</tr>
</tfoot>
</table>
<table class=" lightable-classic" style="font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;">
<caption>
Balance Table by Cutoff (Before vs After)
</caption>
<thead>
<tr>
<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Before
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
After
</div>
</th>
<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
Stat
</div>
</th>
</tr>
<tr>
<th style="text-align:left;">
Variable
</th>
<th style="text-align:right;">
Mean
</th>
<th style="text-align:right;">
SD
</th>
<th style="text-align:right;">
Mean
</th>
<th style="text-align:right;">
SD
</th>
<th style="text-align:right;">
Test
</th>
<th style="text-align:right;">
p-value
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;font-weight: bold;">
Age
</td>
<td style="text-align:right;">
48.40
</td>
<td style="text-align:right;">
15.10
</td>
<td style="text-align:right;">
50.96
</td>
<td style="text-align:right;">
15.56
</td>
<td style="text-align:right;">
t=1.82
</td>
<td style="text-align:right;">
0.071
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
Female
</td>
<td style="text-align:right;">
0.46
</td>
<td style="text-align:right;">
0.50
</td>
<td style="text-align:right;">
0.48
</td>
<td style="text-align:right;">
0.50
</td>
<td style="text-align:right;">
z=0.37
</td>
<td style="text-align:right;">
0.712
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
Education
</td>
<td style="text-align:right;">
3.34
</td>
<td style="text-align:right;">
0.87
</td>
<td style="text-align:right;">
3.13
</td>
<td style="text-align:right;">
0.93
</td>
<td style="text-align:right;">
t=-2.57
</td>
<td style="text-align:right;">
0.011
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
Income
</td>
<td style="text-align:right;">
7.07
</td>
<td style="text-align:right;">
3.28
</td>
<td style="text-align:right;">
6.88
</td>
<td style="text-align:right;">
3.20
</td>
<td style="text-align:right;">
t=-0.56
</td>
<td style="text-align:right;">
0.575
</td>
</tr>
<tr>
<td style="text-align:left;font-weight: bold;">
In-partisans
</td>
<td style="text-align:right;">
0.30
</td>
<td style="text-align:right;">
0.46
</td>
<td style="text-align:right;">
0.29
</td>
<td style="text-align:right;">
0.45
</td>
<td style="text-align:right;">
z=-0.19
</td>
<td style="text-align:right;">
0.848
</td>
</tr>
</tbody>
</table>
</div>
<div id="no-significant-mean-difference-is-found-the-opening-ceremony" class="section level3 unnumbered">
<h3 class="unnumbered">No significant mean difference is found, the
opening ceremony</h3>
<div class="sourceCode" id="cb83"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb83-1"><a href="#cb83-1" tabindex="-1"></a><span class="cf">if</span> (<span class="sc">!</span><span class="st">&quot;cutoff_f&quot;</span> <span class="sc">%in%</span> <span class="fu">names</span>(df_rddop)) {</span>
<span id="cb83-2"><a href="#cb83-2" tabindex="-1"></a>  df_rddop <span class="ot">&lt;-</span> df_rddop <span class="sc">|&gt;</span></span>
<span id="cb83-3"><a href="#cb83-3" tabindex="-1"></a>    <span class="fu">mutate</span>(<span class="at">cutoff_f =</span> <span class="fu">factor</span>(cutoff, <span class="at">levels =</span> <span class="fu">c</span>(<span class="dv">0</span>,<span class="dv">1</span>),</span>
<span id="cb83-4"><a href="#cb83-4" tabindex="-1"></a>                             <span class="at">labels =</span> <span class="fu">c</span>(<span class="st">&quot;before&quot;</span>,<span class="st">&quot;after&quot;</span>)))</span>
<span id="cb83-5"><a href="#cb83-5" tabindex="-1"></a>}</span>
<span id="cb83-6"><a href="#cb83-6" tabindex="-1"></a></span>
<span id="cb83-7"><a href="#cb83-7" tabindex="-1"></a>p_to_stars <span class="ot">&lt;-</span> <span class="cf">function</span>(p){</span>
<span id="cb83-8"><a href="#cb83-8" tabindex="-1"></a>  <span class="cf">if</span> (<span class="fu">is.na</span>(p)) <span class="st">&quot;n.s.&quot;</span></span>
<span id="cb83-9"><a href="#cb83-9" tabindex="-1"></a>  <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.001</span>) <span class="st">&quot;***&quot;</span></span>
<span id="cb83-10"><a href="#cb83-10" tabindex="-1"></a>  <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.01</span>)  <span class="st">&quot;**&quot;</span></span>
<span id="cb83-11"><a href="#cb83-11" tabindex="-1"></a>  <span class="cf">else</span> <span class="cf">if</span> (p <span class="sc">&lt;</span> <span class="fl">0.05</span>)  <span class="st">&quot;*&quot;</span></span>
<span id="cb83-12"><a href="#cb83-12" tabindex="-1"></a>  <span class="cf">else</span> <span class="st">&quot;n.s.&quot;</span></span>
<span id="cb83-13"><a href="#cb83-13" tabindex="-1"></a>}</span>
<span id="cb83-14"><a href="#cb83-14" tabindex="-1"></a></span>
<span id="cb83-15"><a href="#cb83-15" tabindex="-1"></a>summ_ci <span class="ot">&lt;-</span> <span class="cf">function</span>(df, var){</span>
<span id="cb83-16"><a href="#cb83-16" tabindex="-1"></a>  df <span class="sc">|&gt;</span></span>
<span id="cb83-17"><a href="#cb83-17" tabindex="-1"></a>    <span class="fu">group_by</span>(cutoff_f) <span class="sc">|&gt;</span></span>
<span id="cb83-18"><a href="#cb83-18" tabindex="-1"></a>    <span class="fu">summarise</span>(</span>
<span id="cb83-19"><a href="#cb83-19" tabindex="-1"></a>      <span class="at">n     =</span> <span class="fu">sum</span>(<span class="sc">!</span><span class="fu">is.na</span>(.data[[var]])),</span>
<span id="cb83-20"><a href="#cb83-20" tabindex="-1"></a>      <span class="at">mean  =</span> <span class="fu">mean</span>(.data[[var]], <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb83-21"><a href="#cb83-21" tabindex="-1"></a>      <span class="at">sd    =</span> <span class="fu">sd</span>(.data[[var]],   <span class="at">na.rm =</span> <span class="cn">TRUE</span>),</span>
<span id="cb83-22"><a href="#cb83-22" tabindex="-1"></a>      <span class="at">se    =</span> sd <span class="sc">/</span> <span class="fu">sqrt</span>(n),</span>
<span id="cb83-23"><a href="#cb83-23" tabindex="-1"></a>      <span class="at">tcrit =</span> <span class="fu">qt</span>(<span class="fl">0.975</span>, <span class="at">df =</span> <span class="fu">pmax</span>(n <span class="sc">-</span> <span class="dv">1</span>, <span class="dv">1</span>)),</span>
<span id="cb83-24"><a href="#cb83-24" tabindex="-1"></a>      <span class="at">lo95  =</span> mean <span class="sc">-</span> tcrit <span class="sc">*</span> se,</span>
<span id="cb83-25"><a href="#cb83-25" tabindex="-1"></a>      <span class="at">up95  =</span> mean <span class="sc">+</span> tcrit <span class="sc">*</span> se,</span>
<span id="cb83-26"><a href="#cb83-26" tabindex="-1"></a>      <span class="at">.groups =</span> <span class="st">&quot;drop&quot;</span></span>
<span id="cb83-27"><a href="#cb83-27" tabindex="-1"></a>    )</span>
<span id="cb83-28"><a href="#cb83-28" tabindex="-1"></a>}</span>
<span id="cb83-29"><a href="#cb83-29" tabindex="-1"></a></span>
<span id="cb83-30"><a href="#cb83-30" tabindex="-1"></a>make_mean_plot_cont <span class="ot">&lt;-</span> <span class="cf">function</span>(df, var, main_title, <span class="at">ylab =</span> <span class="st">&quot;Mean&quot;</span>) {</span>
<span id="cb83-31"><a href="#cb83-31" tabindex="-1"></a>  </span>
<span id="cb83-32"><a href="#cb83-32" tabindex="-1"></a>  s <span class="ot">&lt;-</span> <span class="fu">summ_ci</span>(df, var)</span>
<span id="cb83-33"><a href="#cb83-33" tabindex="-1"></a>  t_out <span class="ot">&lt;-</span> <span class="fu">t.test</span>(df[[var]] <span class="sc">~</span> df<span class="sc">$</span>cutoff_f)</span>
<span id="cb83-34"><a href="#cb83-34" tabindex="-1"></a>  pval  <span class="ot">&lt;-</span> t_out<span class="sc">$</span>p.value</span>
<span id="cb83-35"><a href="#cb83-35" tabindex="-1"></a>  stars <span class="ot">&lt;-</span> <span class="fu">p_to_stars</span>(pval)</span>
<span id="cb83-36"><a href="#cb83-36" tabindex="-1"></a>  </span>
<span id="cb83-37"><a href="#cb83-37" tabindex="-1"></a>  ymax  <span class="ot">&lt;-</span> <span class="fu">max</span>(s<span class="sc">$</span>up95, <span class="at">na.rm =</span> <span class="cn">TRUE</span>)</span>
<span id="cb83-38"><a href="#cb83-38" tabindex="-1"></a>  y_sig <span class="ot">&lt;-</span> ymax <span class="sc">+</span> <span class="fl">0.05</span> <span class="sc">*</span> <span class="fu">max</span>(<span class="dv">1</span>, ymax)</span>
<span id="cb83-39"><a href="#cb83-39" tabindex="-1"></a>  y_txt <span class="ot">&lt;-</span> ymax <span class="sc">+</span> <span class="fl">0.10</span> <span class="sc">*</span> <span class="fu">max</span>(<span class="dv">1</span>, ymax)</span>
<span id="cb83-40"><a href="#cb83-40" tabindex="-1"></a>  </span>
<span id="cb83-41"><a href="#cb83-41" tabindex="-1"></a>  <span class="fu">ggplot</span>(s, <span class="fu">aes</span>(<span class="at">x =</span> cutoff_f, <span class="at">y =</span> mean)) <span class="sc">+</span></span>
<span id="cb83-42"><a href="#cb83-42" tabindex="-1"></a>    <span class="fu">geom_bar</span>(<span class="at">stat =</span> <span class="st">&quot;identity&quot;</span>, <span class="at">width =</span> <span class="fl">0.6</span>, <span class="at">fill =</span> <span class="st">&quot;grey70&quot;</span>, <span class="at">color =</span> <span class="st">&quot;grey20&quot;</span>) <span class="sc">+</span></span>
<span id="cb83-43"><a href="#cb83-43" tabindex="-1"></a>    <span class="fu">geom_errorbar</span>(<span class="fu">aes</span>(<span class="at">ymin =</span> lo95, <span class="at">ymax =</span> up95), <span class="at">width =</span> <span class="fl">0.15</span>) <span class="sc">+</span></span>
<span id="cb83-44"><a href="#cb83-44" tabindex="-1"></a>    <span class="fu">geom_text</span>(<span class="fu">aes</span>(<span class="at">label =</span> <span class="fu">sprintf</span>(<span class="st">&quot;%.2f</span><span class="sc">\n</span><span class="st">[%.2f, %.2f]&quot;</span>, mean, lo95, up95)),</span>
<span id="cb83-45"><a href="#cb83-45" tabindex="-1"></a>              <span class="at">vjust =</span> <span class="sc">-</span><span class="fl">1.1</span>, <span class="at">size =</span> <span class="dv">3</span>) <span class="sc">+</span></span>
<span id="cb83-46"><a href="#cb83-46" tabindex="-1"></a>    ggsignif<span class="sc">::</span><span class="fu">geom_signif</span>(</span>
<span id="cb83-47"><a href="#cb83-47" tabindex="-1"></a>      <span class="at">comparisons =</span> <span class="fu">list</span>(<span class="fu">c</span>(<span class="st">&quot;before&quot;</span>,<span class="st">&quot;after&quot;</span>)),</span>
<span id="cb83-48"><a href="#cb83-48" tabindex="-1"></a>      <span class="at">annotations =</span> stars,</span>
<span id="cb83-49"><a href="#cb83-49" tabindex="-1"></a>      <span class="at">y_position  =</span> y_sig,</span>
<span id="cb83-50"><a href="#cb83-50" tabindex="-1"></a>      <span class="at">tip_length  =</span> <span class="dv">0</span>,</span>
<span id="cb83-51"><a href="#cb83-51" tabindex="-1"></a>      <span class="at">textsize    =</span> <span class="fl">4.2</span></span>
<span id="cb83-52"><a href="#cb83-52" tabindex="-1"></a>    ) <span class="sc">+</span></span>
<span id="cb83-53"><a href="#cb83-53" tabindex="-1"></a>    <span class="fu">annotate</span>(<span class="st">&quot;text&quot;</span>, <span class="at">x =</span> <span class="fl">1.5</span>, <span class="at">y =</span> y_txt,</span>
<span id="cb83-54"><a href="#cb83-54" tabindex="-1"></a>             <span class="at">label =</span> <span class="fu">paste0</span>(<span class="st">&quot;Welch&#39;s t-test p = &quot;</span>, <span class="fu">signif</span>(pval, <span class="dv">3</span>)),</span>
<span id="cb83-55"><a href="#cb83-55" tabindex="-1"></a>             <span class="at">size =</span> <span class="fl">4.2</span>) <span class="sc">+</span></span>
<span id="cb83-56"><a href="#cb83-56" tabindex="-1"></a>    ggthemes<span class="sc">::</span><span class="fu">theme_igray</span>() <span class="sc">+</span></span>
<span id="cb83-57"><a href="#cb83-57" tabindex="-1"></a>    <span class="fu">labs</span>(<span class="at">title =</span> main_title, <span class="at">x =</span> <span class="st">&quot;Control/Treatment&quot;</span>, <span class="at">y =</span> ylab) <span class="sc">+</span></span>
<span id="cb83-58"><a href="#cb83-58" tabindex="-1"></a>    <span class="fu">scale_y_continuous</span>(<span class="at">expand =</span> <span class="fu">expansion</span>(<span class="at">mult =</span> <span class="fu">c</span>(<span class="fl">0.02</span>, <span class="fl">0.18</span>))) <span class="sc">+</span></span>
<span id="cb83-59"><a href="#cb83-59" tabindex="-1"></a>    <span class="fu">theme</span>(<span class="at">plot.title =</span> <span class="fu">element_text</span>(<span class="at">size =</span> <span class="dv">10</span>),</span>
<span id="cb83-60"><a href="#cb83-60" tabindex="-1"></a>          <span class="at">axis.title.x =</span> <span class="fu">element_text</span>(<span class="at">margin =</span> <span class="fu">margin</span>(<span class="at">t =</span> <span class="dv">6</span>)))</span>
<span id="cb83-61"><a href="#cb83-61" tabindex="-1"></a>}</span>
<span id="cb83-62"><a href="#cb83-62" tabindex="-1"></a></span>
<span id="cb83-63"><a href="#cb83-63" tabindex="-1"></a>p_cabap <span class="ot">&lt;-</span> <span class="fu">make_mean_plot_cont</span>(df_rddop, <span class="st">&quot;cabap&quot;</span>,</span>
<span id="cb83-64"><a href="#cb83-64" tabindex="-1"></a>                               <span class="st">&quot;Cabinet approval (mean; 95% CI)&quot;</span>,</span>
<span id="cb83-65"><a href="#cb83-65" tabindex="-1"></a>                               <span class="at">ylab =</span> <span class="st">&quot;Mean Cabinet Approval&quot;</span>)</span>
<span id="cb83-66"><a href="#cb83-66" tabindex="-1"></a>p_ftj   <span class="ot">&lt;-</span> <span class="fu">make_mean_plot_cont</span>(df_rddop, <span class="st">&quot;ftj&quot;</span>,</span>
<span id="cb83-67"><a href="#cb83-67" tabindex="-1"></a>                               <span class="st">&quot;Feeling thermometer (mean; 95% CI)&quot;</span>,</span>
<span id="cb83-68"><a href="#cb83-68" tabindex="-1"></a>                               <span class="at">ylab =</span> <span class="st">&quot;Mean Feeling Thermometer&quot;</span>)</span>
<span id="cb83-69"><a href="#cb83-69" tabindex="-1"></a></span>
<span id="cb83-70"><a href="#cb83-70" tabindex="-1"></a></span>
<span id="cb83-71"><a href="#cb83-71" tabindex="-1"></a><span class="fu">grid.arrange</span>(p_cabap, p_ftj, <span class="at">ncol =</span> <span class="dv">2</span>)</span></code></pre></div>
<p><img role="img" 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" width="672" /></p>
</div>
<div id="no-significant-effect-differences-occurred-near-the-cutoff-opening-ceremony" class="section level3 unnumbered">
<h3 class="unnumbered">No significant effect differences occurred near
the cutoff, opening ceremony</h3>
<div class="sourceCode" id="cb84"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb84-1"><a href="#cb84-1" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> <span class="fu">cbind</span>(df_rddop<span class="sc">$</span>age, df_rddop<span class="sc">$</span>female, df_rddop<span class="sc">$</span>income, df_rddop<span class="sc">$</span>education,</span>
<span id="cb84-2"><a href="#cb84-2" tabindex="-1"></a>                    df_rddop<span class="sc">$</span>psu_rul)</span>
<span id="cb84-3"><a href="#cb84-3" tabindex="-1"></a></span>
<span id="cb84-4"><a href="#cb84-4" tabindex="-1"></a>results_judorakuten <span class="ot">&lt;-</span> <span class="fu">list</span>()</span>
<span id="cb84-5"><a href="#cb84-5" tabindex="-1"></a></span>
<span id="cb84-6"><a href="#cb84-6" tabindex="-1"></a>outcome_vars <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;cabap&quot;</span>, <span class="st">&quot;ftj&quot;</span>)</span>
<span id="cb84-7"><a href="#cb84-7" tabindex="-1"></a>outcome_prefix <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;c&quot;</span>, <span class="st">&quot;f&quot;</span>)</span>
<span id="cb84-8"><a href="#cb84-8" tabindex="-1"></a></span>
<span id="cb84-9"><a href="#cb84-9" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(outcome_vars)) {</span>
<span id="cb84-10"><a href="#cb84-10" tabindex="-1"></a>  outcome_var <span class="ot">&lt;-</span> outcome_vars[i]</span>
<span id="cb84-11"><a href="#cb84-11" tabindex="-1"></a>  prefix <span class="ot">&lt;-</span> outcome_prefix[i]</span>
<span id="cb84-12"><a href="#cb84-12" tabindex="-1"></a>  </span>
<span id="cb84-13"><a href="#cb84-13" tabindex="-1"></a>  safe_rdrobust <span class="ot">&lt;-</span> <span class="cf">function</span>(..., prefix_suffix) {</span>
<span id="cb84-14"><a href="#cb84-14" tabindex="-1"></a>    <span class="fu">tryCatch</span>({</span>
<span id="cb84-15"><a href="#cb84-15" tabindex="-1"></a>      <span class="fu">rdrobust</span>(..., <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb84-16"><a href="#cb84-16" tabindex="-1"></a>    }, <span class="at">error =</span> <span class="cf">function</span>(e) {</span>
<span id="cb84-17"><a href="#cb84-17" tabindex="-1"></a>      <span class="fu">message</span>(<span class="fu">paste0</span>(<span class="st">&quot;Error in &quot;</span>, prefix_suffix, <span class="st">&quot;: &quot;</span>, e<span class="sc">$</span>message))</span>
<span id="cb84-18"><a href="#cb84-18" tabindex="-1"></a>      <span class="cn">NULL</span></span>
<span id="cb84-19"><a href="#cb84-19" tabindex="-1"></a>    })</span>
<span id="cb84-20"><a href="#cb84-20" tabindex="-1"></a>  }</span>
<span id="cb84-21"><a href="#cb84-21" tabindex="-1"></a>  </span>
<span id="cb84-22"><a href="#cb84-22" tabindex="-1"></a>  results_judorakuten[[<span class="fu">paste0</span>(<span class="st">&quot;res1&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rddop[[outcome_var]], df_rddop<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res1&quot;</span>, prefix))</span>
<span id="cb84-23"><a href="#cb84-23" tabindex="-1"></a>  results_judorakuten[[<span class="fu">paste0</span>(<span class="st">&quot;res2&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rddop[[outcome_var]], df_rddop<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;msesum&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res2&quot;</span>, prefix))</span>
<span id="cb84-24"><a href="#cb84-24" tabindex="-1"></a>  results_judorakuten[[<span class="fu">paste0</span>(<span class="st">&quot;res3&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rddop[[outcome_var]], df_rddop<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res3&quot;</span>, prefix))</span>
<span id="cb84-25"><a href="#cb84-25" tabindex="-1"></a>  results_judorakuten[[<span class="fu">paste0</span>(<span class="st">&quot;res4&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rddop[[outcome_var]], df_rddop<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;uni&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res4&quot;</span>, prefix))</span>
<span id="cb84-26"><a href="#cb84-26" tabindex="-1"></a>  results_judorakuten[[<span class="fu">paste0</span>(<span class="st">&quot;res5&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rddop[[outcome_var]], df_rddop<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;cerrd&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res5&quot;</span>, prefix))</span>
<span id="cb84-27"><a href="#cb84-27" tabindex="-1"></a>  results_judorakuten[[<span class="fu">paste0</span>(<span class="st">&quot;res6&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rddop[[outcome_var]], df_rddop<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">q =</span> <span class="dv">2</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res6&quot;</span>, prefix))</span>
<span id="cb84-28"><a href="#cb84-28" tabindex="-1"></a>  results_judorakuten[[<span class="fu">paste0</span>(<span class="st">&quot;res7&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">safe_rdrobust</span>(df_rddop[[outcome_var]], df_rddop<span class="sc">$</span>running_var2, <span class="at">covs =</span> covariates, <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">p =</span> <span class="dv">2</span>, <span class="at">q =</span> <span class="dv">3</span>, <span class="at">prefix_suffix =</span> <span class="fu">paste0</span>(<span class="st">&quot;res7&quot;</span>, prefix))</span>
<span id="cb84-29"><a href="#cb84-29" tabindex="-1"></a>}</span>
<span id="cb84-30"><a href="#cb84-30" tabindex="-1"></a></span>
<span id="cb84-31"><a href="#cb84-31" tabindex="-1"></a></span>
<span id="cb84-32"><a href="#cb84-32" tabindex="-1"></a>p1 <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rddop<span class="sc">$</span>cabap, df_rddop<span class="sc">$</span>running_var2,</span>
<span id="cb84-33"><a href="#cb84-33" tabindex="-1"></a>             <span class="at">covs =</span> <span class="fu">cbind</span>(df_rddop<span class="sc">$</span>age, df_rddop<span class="sc">$</span>female, df_rddop<span class="sc">$</span>income, df_rddop<span class="sc">$</span>education, df_rddop<span class="sc">$</span>psu_rul),</span>
<span id="cb84-34"><a href="#cb84-34" tabindex="-1"></a>             <span class="at">x.lim =</span> cutoff_range, </span>
<span id="cb84-35"><a href="#cb84-35" tabindex="-1"></a>             <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb84-36"><a href="#cb84-36" tabindex="-1"></a>             <span class="at">y.lab =</span> <span class="st">&quot;Cabinet approval&quot;</span>,</span>
<span id="cb84-37"><a href="#cb84-37" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Cutoff: The Gold medal confiramtion of women&#39;s Judo 48kg&quot;</span>,</span>
<span id="cb84-38"><a href="#cb84-38" tabindex="-1"></a>             <span class="at">c =</span> <span class="dv">0</span>,</span>
<span id="cb84-39"><a href="#cb84-39" tabindex="-1"></a>             <span class="at">ci =</span> <span class="dv">95</span>) </span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
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xsq945iqUAH2MuqNN/Aztcy9qCb9Le9m8WbjL1LzpK0rAO/aue+eP/mpB5rPOA+nt2XufPgNh7b1uOj33aQ5W/z7PcbLu7tfbK0v7PjUJOrSserrtQjo2ejAiWEhGb1pc5k9CBQQq54Ok+hzZcoZHNBoIRc8XQ+93eSPjNFNhEESsgVT32pU/VWbH3BIOeQIoNACbnq6VxSxRPQ2CBQQq56FldmpS+NIhsJAiXk6qf5/fU/SF8aRTYSBEoIIc4gUEIIcQaBEkKIMwiUEEKcQaCEEOIMAiWEEGcQKCGEOINACSHEGQRKCCHOIFBCCHEGgRJCiDMIlBBCnEGghBDiDAIlhBBnNiLQbxJCyE4mQqAjbmuifI78mY6sQytr01sCF6KVbM8kBFoSBBqOprcALkQjUAsy4rYQqAAuRNNbABeiEagFGXFbCFQAF6LpLYAL0QjUgoy4LQQqgAvR9BbAhWgEakFG3BYCFcCFaHoL4EI0ArUgI24LgQrgQjS9BXAhGoFakBG3hUAFcCGa3gK4EI1ALciI20KgArgQTW8BXIhGoBZkxG0hUAFciKa3AC5EI1ALMuK2EKgALkTTWwAXohGoBRlxWwhUABei6S2AC9EI1IKMuC0EKoAL0fQWwIVoBGpBRtwWAhXAhWh6C+BCNAK1ICNuC4EK4EI0vQVwIRqBWpARt4VABXAhmt4CuBCNQC3IiNtCoAK4EE1vAVyIRqAWZMRtIVABXIimtwAuRCNQCzLithCoAC5E01sAF6IRqAUZcVsIVAAXouktgAvRCNSCjLgtBCqAC9H0FsCFaARqQUbcFgIVwIVoegvgQjQCtSAjbguBCuBCNL0FcCEagVqQEbeFQAVwIZreArgQjUAtyIjbQqACuBBNbwFciEagFmTEbSFQAVyIprcALkQjUAsy4rYQqAAuRNNbABeiEagFGXFbCFQAF6LpLYAL0QjUgoy4LQQqgAvR9BbAhWgEakFG3BYCFcCFaHoL4EI0ArUgI24LgQrgQjS9BXAhGoFakBG3hUAFcCGa3gK4EI1ALciI20KgArgQTW8BXIhGoBZkxG0hUAFciKa3AC5EI1ALMuK2EKgALkTTWwAXohGoBekC1skFApXAhWh6C+BCNAK1IF0AAnWRWVACuBBN73i2Z9KWvYS/XI8IVAAXouktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszKUagn+VmMll/BCGExIRnoCXhGWg4mt4CuBDNM1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kwaXaBf3v3uHwaQ7NkINEVmQQngQjS949meSWML9OufHiHQTZBZUAK4EE3veLZn0tgC/fQIgW6EzIISwIVoesezPZNGFuiXdxHoZsgsKAFciKZ3PNszaVyBTl/A/w3vgW6EzIISwIVoesezPZPGFeiDo+9xEmkzZBaUAC5E0zue7Zk0qkA/n7587wr0m/N8lpvJZP0RhBASkzEF+tUbL/z8AoESQvYlYwr0wdGPuA50U2Re0gngQjS949meSSMK9NP6/DsC3QyZBSWAC9H0jmd7Jo0n0C/vTl/AI9BNkVlQArgQTe94tmfSeAL99KjNt3/bg2RvBIGmyCwoAVyIpnc82zMJgZYEgYaj6S2AC9H7K9AmvITfDJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagSLQjZBZUAK4EE3veLZnEgItCQINR9NbABei916gJiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkItCQINBxNbwFciEagFiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkItCQINBxNbwFciEagFiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkItCQINBxNbwFciEagFiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkItCQINBxNbwFciEagFiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkItCQINBxNbwFciEagFiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkItCQINBxNbwFciEagFiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkItCQINBxNbwFciEagFiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkItCQINBxNbwFciEagFiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkItCQINBxNbwFciEagFiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkItCQINBxNbwFciEagFiR7JAJNkVlQArgQTe94tmcSAi0JAg1H01sAF6IRqAXJHolAU2QWlAAuRNM7nu2ZhEBLgkDD0fQWwAq0UOQAACAASURBVIVoBGpBskci0BSZBSWAC9H0jmd7JiHQkiDQcDS9BXAhetcEev7Wq8N8/1EhJHskAk2RWVACuBBN73i2Z9JlAn3y4mSYp+4XQrJHItAUmQUlgAvR9I5neyYh0JIg0HA0vQVwIXrXBDpKEOj6ZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smFQj0/PdNHv7Z/UJI9kgEmiKzoARwIZre8WzPpFyBPn6Tk0gIVICmtwAuRO+oQJdPxl+/XwjJHolAU2QWlAAuRNM7nu2ZlCnQk8nk4LlXDqv/TQ5eL4Zkj0SgKTILSgAXoukdz/ZMyhPo+b3J9UfV/9+pXHq98INICHQEMgtKABei6R3P9kzKE+iTFw/euajceXv6/8eVRssg2SMRaIrMghLAhWh6x7M9k3IFWp83Op3caP+/CJI9EoGmyCwoAVyIpnc82zOpUKDVq/cnL5a+hkeg65NZUAK4EE3veLZnUu57oPVL+LPDyqONTUsg2SMRaIrMghLAhWh6x7M9kzLPwh/X737O3gqdabQIkj0SgabILCgBXIimdzzbMylToGeHk+c/rE7D365kykv4eDILSgAXoukdz/ZMyv0k0nH9+aPTyeTgcFI/Gy2CZI9EoCkyC0oAF6LpHc/2TMr+LPzvqhfu58f1B5G4DjSezIISwIVoesezPZMKbibyj1Nvnn908+Zrpf5EoCOQWVACuBBN73i2Z1LM7ew+y81ksv4IQgiJCfcDLQnPQMPR9BbAhejdfAb65MXnP/RsfQ7JHolAU2QWlAAuRNM7nu2ZlCvQyeSg/L3PFpI9EoGmyCwoAVyIpnc82zMp85NI7x5Wp9+ffduDQKBjkFlQArgQTe94tmdS9nugD1+uFHrgeimPQNcns6AEcCGa3vFsz6SS34n00TOVQ59+ncuYBGQWlAAuRNM7nu2ZVHYW/ov365fyf8pHOcPJLCgBXIimdzzbM6n4Mqbqt8txM5F4MgtKABei6R3P9kwqFOj5+8/wWzklZBaUAC5E0zue7ZlUItDzhy/53gRFoOuTWVACuBBN73i2Z1K+QD/2n4ZHoOuTWVACuBBN73i2Z1KmQB+/WZ89uua7EBSBrk9mQQngQjS949meSfmfRFrjo0gIdH0yC0oAF6LpHc/2TMoW6PO/9my+gWSPRKApMgtKABei6R3P9kzK/Cjnr9yfg68h2SMRaIrMghLAhWh6x7M9k7idXUkQaDia3gK4EL27Av3i/ZuTycHN1z5xQLJHItAUmQUlgAvR9I5neyZlC/RkMk/pr5RDoGOQWVACuBBN73i2Z1KuQCt/Pv3cq6884zEoAl2fzIISwIVoesezPZPyfy/89dkV9I/vTQ7eKYVkj0SgKTILSgAXoukdz/ZMyhTo8eJ3GZ/fm9wohWSPRKApMgtKABei6R3P9kzKvIzpXudZ59lh6S+GR6Drk1lQArgQTe94tmdS7oX0nRswLf0lD5I9EoGmyCwoAVyIpnc82zMJgZYEgYaj6S2AC9G7KdDze5M77V9OJ7yEjyezoARwIZre8WzPJE4ilQSBhqPpLYAL0Tsq0LPDybXZ3UQ+fonLmBRkFpQALkTTO57tmVRyIf3k5s2bro8iIdD1ySwoAVyIpnc82zMp+6OcHx02n+Q8eL0ckj0SgabILCgBXIimdzzbMyn/ZiLnD1+ZPgN97m3Hje0Q6PpkFpQALkTTO57tmcTt7EqCQMPR9BbAhegdFeix53fJLSDZIxFoisyCEsCFaHrHsz2Tsn+lxx3zC5mQ7JEINEVmQQngQjS949meSY5PIjkg2SMRaIrMghLAhWh6x7M9kxw3E3FAskci0BSZBSWAC9H0jmd7JmW+B3oyvx2oKwh0fTILSgAXoukdz/ZMyhToF+/VN6Rv8n0+Cx9OZkEJ4EI0vePZnknZJ5G64W5M8WQWlAAuRNM7nu2ZhEBLgkDD0fQWwIXo3RTomkGg65NZUAK4EE3veLZnEgItCQINR9NbABeid1ugv/cgEOgYZBaUAC5E0zue7ZmUL9CHL9f3YvJ8phOBrk9mQQngQjS949meSbkCPb/XnkK6VXw/JgS6PpkFJYAL0fSOZ3smZQq08ufBc3/3wS9eOZwU/0YPBDoCmQUlgAvR9I5neyZlfxJp8q3ZE8/z9ybFNxZBoOuTWVACuBBN73i2Z1L2b+Vc/B6PY36pnIDMghLAhWh6x7M9k3IvpO/cTOTskAvp48ksKAFciKZ3PNszyXE7u/J72yHQ9cksKAFciKZ3PNszyXE7u7PD69xMJJzMghLAhWh6x7M9k7JPIt0w/5wJyR6JQFNkFpQALkTTO57tmZR/GdP88s+T4nuJINARyCwoAVyIpnc82zMp8yX8W9X1n8+99sEvq/8+W3pXUAS6PpkFJYAL0fSOZ3smuW5nV3pTOwS6PpkFJYAL0fSOZ3smIdCSjPfdNY5nfkbbi8zs6YKitwC9mwJdMwh0EASaiUYkArgQjUAtSPbIvRHo6gxLsqAUcCGa3vFszyTuB1oSBBqOprcALkTvrkC5HygCFaDpLYAL0bsqUO4HWgWBhqPpLYAL0TsqUO4HWgeBhqPpLYAL0TsqUO4HWgeBhqPpLYAL0bspUO4HOgsCDUfTWwAXondToNwPdBYEGo6mtwAuRO+qQLkfaBUEGo6mtwAuRO+mQLkf6CwINBxNbwFciN5NgXI/0FkQaDia3gK4EL2jAuV+oHUQaDia3gK4EL2bAuV+oLMg0HA0vQVwIXo3Bcrt7GZBoOFoegvgQjQCtSDZu4NA6yDQFk1vAVyI3k2BrhmhQMe97yYCDUfTWwAXohGoBckeiUCbvR6QWVACuBBN73i2Z9KuC3TcbSHQcDS9BXAheocFev77Jg//7H4hJHskAq2DQFs0vQVwIXpXBfr4TdfpowaSPRKB1kGgLZreArgQvaMCXT4Nf/1+ISR7JAKtg0BbNL0FcCF6RwV6Ut1P+ZXD6n+Tg9eLIdkjEWgdBNqi6S2AC9G7KdDze5Prj6r/v1O5tPReIgi0NAi0RdNbABeid1Ogzf1AT+rbKh/vzB3pEWhe9nRB0VuA3lWB1ueNTuv7MJ3uzN2YEGhe9nRB0VuA3nGBVq/en7y4K/cDRaB52dMFRW8BejcF2txQefbLPDZ4R/pvTCbfuGQIAt00esyPbq0dRKKAC9G7KdCL4/rdz9lboZv7nUjfqAR6iUER6KbRCLSFC9H0jmd7JmUK9Oxw8vyHzS/nPC4+DZ8p0G/MBLraoAg0Pi07/lZY29FbgKZ3PNszKfeTSMf1549OJ5ODw0nnVxxnQrJGfWMu0JUGRaDxQaDxaHrHsz2Tsj8L/7vqhfv5cf1BpM1cB4pA2yDQFr0VvQVoesezPZMKbibyj1Nvnn908+Zrpf5EoKVBoC16K3oL0PSOZ3smbdHt7BBoGwTaorei96ps5qzb9vfeEBqBWpC8YZxEmgeBtuit6L0qCHRUNAK1IJnjuIypCQJt0VvR2x/vEbvqvd1oBGpBcgdyIf0sCLRFb0VvfxBoIRqBWpDskXyUsw4CbdFb0dudjGcECfTV7u1HI1ALkj1y5Qr91yoIND4I1Jmc96QS6Cvdew00ArUg2SPNFfqv3SDQ+CBQX7LOiibQV7n3OmgEakGyR3ZX6L8O0h+xbhBoXhCoK3nX5SXQV7j3WmgEakGyR04mQ23OxLkYMd5+batAjXfOtmNBIdCSIFAHejcFev7W9xcfPzp75U9Hv5nI4gV6wpuLHdx9gVrvnG3HgkKgJUGgDvRuCnTpFqBj3Q/Ueq65B2fhrdrDw5AzKiad2u5zyv5cZZEgUDub+ejB+okS6Dr3A121TGt8pEDLfZA6xGNLa5sE2o1nv9b7Hl1pkXASycx+CXT5N8LPUnw/0OylFSnQomtM1hNPiVDs5y2yBbW07zFi34reNXzdDXAZUzE6/l2iBdsz6fJnoKdDgRb/Vs5sfQQK1H56sP6SH2G/tkmgNbz6P+cL0nGcGnLkB+FC+nj07gn0/P+8+uorhwfPvTrPD35dDMkeGSfQqQhKnlDNJkU8snZMoOVJfAfWeuHn8y0f5QyP4G32NlHvgTog2SM3KVBrTRY+pQl5ZJlPjfULKkygy+jOFQAjCLQw6+8/Ai2K/z2PERJ1GZMDkj1yZIGuXh7lPtjny5jUAl2ZzEeF06NrOBeBlmSNs24jZGXt1AO/6EL6809K96mBZI9cT6Alj22XD/b6QnrJozuv94Ze+GUZc6xnxp7eK7PWjq1N90TzM7qN60DlC/Thy9XvlXvyb8t/o8cGBVr2dGApHh/s90c5Fa+vsnpvaseyjvlanspSdHnW37HxD2ZGNi7Q1UfNdaByBXr+bjV6KtAXJ9ful+732AJd+bjJjmPZ7bdAt/VC+o09NY485mM79LLs0qXGIUnsWMGvNb727w6fun/+Xzb2WzlrfIZAl/a/cE+WMt6F9GNnSwW6nR/l3Nzzliv7kUa/D+TZ6H5dcsyTX1nxEMsU6Olk8npzLv6jw+R1oF//7O7R0Q9/M4Qk96yfTIFmb29dWi8INByNQMfPZUeMk0i9rC/Q48nt9mKmk8kNe9BXbxxV+fZvB5DknvWzB5+FzwkCbdEIdPxsrUC39TKmtQV6fu/gnVagyc/CPzj67m8u/vjTo+/+oQ9J73QvCLQOAm3RCHT8bK9ApRfSb/Ak0kydjUBTV9V/ebd+7vnVGy/8vPcVBFoYBNqi9+UkUh+9UfYlR2xPP8qpFuinR99r/vuj3lcQaGEQaIve+suYNpO9vA70Yos/C7/ehfTn96oTR405TxOn4R8c/bj+7+eNSDuQVXu2jEegVRBoi1ZeSI9AN5Jt3bENfpRzduJoJtCpTM2TSF//tHnp/uXd+Zug35zns9xMJuuPyM+Y2xo127pj27pf27tjZJi1BKre+X4yBXp2OLn1qBbo45cm1QklBLrJbOuObet+be+OkR1P7oX0J1P53zw8eO6Z6X9vmyM6Au1fyMRL+MLwEr5Fj3M3Jid8rBYO9FZ8vwXo3fydSNP87nD+YLT9aT0DbSHZu4NA6yDQFo1ABXAhemcFevHF+zenj8Snn/8w8XUEOl4QaIveit4CNL3j2Z5JI/5eeM7CjxYE2qK3orcATe94tmfSiAKdX//JdaBrRyhQwSvlVUEkCrgQvcsC/f089n2V+STSaEGg8yASBVyI3lWBPn6zs07sTyJ9/dOj7/BZ+FGyrS/hBWh6C+BC9I4KdPm3wyduJvJH7sY0UhBoi6a3AC5E76hATyaTaz/4YJ5fJe6o/MefTf35w/7zTwRaHATaouktgAvRuynQ1Mc3cyHZIxFoHQTaouktgAvRuynQJy/aH9/MhWSPRKB1EGiLprcALkTvqkATb3tmQrJHItA6CLRF01sAF6J3U6CzO9K7g0ALg0BbNL0FcCF6NwV6cZL6BHweJHskAq2DQFs0vQVwIXpHBTp9Cvq6Z/MNJHskAq2DQFs0vQVwIXo3BXr+1iuTycFzrzb5fuEvhkeghUGgLZreArgQvZsCXb6OPnUhfRqSPRKB1kGgLZreArgQjUAtSPZIBFoHgbZoegvgQvRuCnTNCAU67r0xEGg4mt4CuBCNQC1I9kgE2uz1gMyCEsCFaHrHsz2Tdl2g4waBhqPpLYAL0bsm0PO3qnPu0//vZlfOwhcHgYaj6S2AC9G7JtAnL1anjHb0JFJxEGg4mt4CuBCNQC1I9kgEWgeBtmh6C+BC9K4JdJQg0MIg0BZNbwFciEagFiR7JAKtg0BbNL0FcCF6pwV6/nsPAoEWB4G2aHoL4EL07gr04cv1+5/Pf+iAZI9EoHUQaIumtwAuRO+qQM/vtaeQbhVexIRAi4NAWzS9BXAhekcFWvnz4LmffPCLV6YGLf71SAh0kHE/IbXh7OmCorcAvaMCPZ3M76h8/t5kcqcUkj0SgSLQZTQiEcCF6N0U6PQJ6OKO9MfFT0ER6PpkFpQALkTTO57tmeT4rZxnh1xIH09mQQngQjS949meSY7fyln+KzoR6PpkFpQALkTTO57tmeT4rZxnh9e5mUg4mQUlgAvR9I5neyZl/1bOG50/l/6GTgS6PpkFJYAL0fSOZ3smZQr0yYuTbzVPO0+K7yWCQEcgs6AEcCGa3vFsz6SM+4HWeWV2HegvXz2cTJ7jfqDxZBaUAC5E0zue7ZmUcTu7YTiJFE9mQQngQjS949meSQi0JAg0HE1vAVyI3jWBjhIEuj6ZBSWAC9H0jmd7JiHQkiDQcDS9BXAhGoFakOyRCDRFZkEJ4EI0vePZnkkFAj3/fZOHf3a/EJI9EoGmyCwoAVyIpnc82zMpV6CP3+QkEgIVoOktgAvROyrQ5ZPx1+8XQrJHItAUmQUlgAvR9I5neyZlf5RzcvDcK4fV/yYHrxdDskci0BSZBSWAC9H0jmd7JmXfD/T6o+r/71QuLb2XCAIdgcyCEsCFaHrHsz2Tiu4HOruNyDF3pBeQWVACuBBN73i2Z1LR/UBP63synXJHegGZBSWAC9H0jmd7JhUKtHr1/uRF7gcaT2ZBCeBCNL3j2Z5JRTdUnv0yD+5IryCzoARwIZre8WzPpMyz8Mf1u5+zt0L5nUgKMgtKABei6R3P9kzKFOjZ4eT5D5tfznlcfBoega5PZkEJ4EI0vePZnkm5n0Q6rj9/dDqZHBxO+JUeAjILSgAXoukdz/ZMyv4s/O+qF+7nx/UHkbgONJ7MghLAhWh6x7M9kwpuJvKPU2+ef3Tz5mul/kSgI5BZUAK4EE3veLZnErezKwkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmUJ9AvPpn99+zZt4tfv18g0DHILCgBXIimdzzbMylHoI9fmp93r27KVHwvJgQ6BpkFJYAL0fSOZ3smZQj0o8PJ/NPv71Un4UsvYkKgY5BZUAK4EE3veLZn0uUCPZ0689qvm7+cvzv9W+nNmBDoCGQWlAAuRNM7nu2ZdKlAq3vRd59znpT/Rg8EOgKZBSWAC9H0jmd7Jl0q0JPehfPn9/gkkoLMghLAhWh6x7M9ky4TaOXL5Zfsp+UfRUKg65NZUAK4EE3veLZn0mUCnb6Cr+9kt8jZIb+VU0BmQQngQjS949meSRkC7ely+C+XQ7JHItAUmQUlgAvR9I5neyYh0JIg0HA0vQVwIXrXBHp+z3gJz3ug8WQWlAAuRNM7nu2ZdOlZ+OP+SfeTCb9UTkBmQQngQjS949meSZcKtH/SncuYNGQWlAAuRNM7nu2ZVHwh/TEX0kvILCgBXIimdzzbMynvo5y35s9BH7/p+TA8Al2fzIISwIVoesezPZMybiZyUt1B5NpPPvjgg/dfqv5Y+g4oAh2DzIISwIVoesezPZNybmf3u8PJIgd/4YBkj0SgKTILSgAXoukdz/ZMyrqhcv3CfabPW594INkjEWiKzIISwIVoesezPZNyf6XHx79869UffOCx5wUCHYPMghLAhWh6x7M9k/idSCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1ALkj0SgabILCgBXIimdzzbMwmBlgSBhqPpLYAL0QjUgmSPRKApMgtKABei6R3P9kxCoCVBoOFoegvgQjQCtSDZIxFoisyCEsCFaHrHsz2TEGhJEGg4mt4CuBCNQC1I9kgEmiKzoARwIZre8WzPJARaEgQajqa3AC5EI1AL8lluJpP1RxBCSEx4BloSnoGGo+ktgAvRPAO1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM8kBFoSBBqOprcALkQjUAuSPRKBpsgsKAFciKZ3PNszCYGWBIGGo+ktgAvRCNSCZI9EoCkyC0oAF6LpHc/2TEKgJUGg4Wh6C+BCNAK1INkjEWiKzIISwIVoesezPZMQaEkQaDia3gK4EI1ALUj2SASaIrOgBHAhmt7xbM+k0QX65d3v/mEAyZ6NQFNkFpQALkTTO57tmTS2QL/+6REC3QSZBSWAC9H0jmd7Jo0t0E+PEOhGyCwoAVyIpnc82zNpZIF+eReBbobMghLAhWh6x7M9k8YV6PQF/N/wHuhGyCwoAVyIpnc82zNpXIE+OPoeJ5E2Q2ZBCeBCNL3j2Z5Jowr08+nL965AvznPZ7mZTNYfQQghMRlToF+98cLPLxAoIWRfMqZAHxz9iOtAN0XmJZ0ALkTTO57tmTSiQD+tz78j0M2QWVACuBBN73i2Z9L6Av38qM6Pv7w7fQGPQDdFZkEJ4EI0vePZnknjCfTTozbf/m0Pkr0xBJois6AEcCGa3vFsz6TxXsIj0I2SWVACuBBN73i2ZxI3EykJAg1H01sAF6IRKALdCJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagSLQjZBZUAK4EE3veLZnEnekl6d7/AAAEihJREFULwkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBckeiUBTZBaUAC5E0zue7ZmEQEuCQMPR9BbAhWgEakGyRyLQFJkFJYAL0fSOZ3smIdCSINBwNL0FcCEagVqQ7JEINEVmQQngQjS949meSQi0JAg0HE1vAVyIRqAWJHskAk2RWVACuBBN73i2ZxICLQkCDUfTWwAXohGoBekC1skFApXAhWh6C+BCNAK1IF0AAnWRWVACuBBN73i2Z9KWvYS/PAhUABei6S2AC9EI1IKMuC0EKoAL0fQWwIVoBGpBRtwWAhXAhWh6C+BCNAK1ICNuC4EK4EI0vQVwIRqBWpARt4VABXAhmt4CuBCNQC3IiNtCoAK4EE1vAVyIRqAWZMRtIVABXIimtwAuRCNQC/LZeJlMRtwYIYSsEZ6BloRnoOFoegvgQjTPQC3IiNtCoAK4EE1vAVyIRqAWZMRtIVABXIimtwAuRCNQCzLithCoAC5E01sAF6IRqAUZcVsIVAAXouktgAvRCNSCjLgtBCqAC9H0FsCFaARqQUbcFgIVwIVoegvgQjQCtSAjbguBCuBCNL0FcCEagVqQEbeFQAVwIZreArgQjUAtyIjbQqACuBBNbwFciEagFmTEbSFQAVyIprcALkQjUAsy4rYQqAAuRNNbABeiEagFGXFbCFQAF6LpLYAL0QjUgoy4LQQqgAvR9BbAhWgEakFG3BYCFcCFaHoL4EI0ArUgI24LgQrgQjS9BXAhGoFakBG3hUAFcCGa3gK4EI1ALciI20KgArgQTW8BXIhGoBZkxG0hUAFciKa3AC5EI1ALMuK2EKgALkTTWwAXohGoBRlxWwhUABei6S2AC9EI1IKMuC0EKoAL0fQWwIVoBGpBRtwWAhXAhWh6C+BCNAK1ICNuC4EK4EI0vQVwIRqBWpARt4VABXAhmt4CuBCNQC3IiNtCoAK4EE1vAVyIRqAmhBBCdjEIlBBCnAkQ6Jjp7/CeZE9r03vPcuV7I9DtzJ7Wpvee5cr3RqDbmT2tTe89y5XvjUC3M3tam957livfG4FuZ/a0Nr33LFe+NwLdzuxpbXrvWa58bwS6ndnT2vTes1z53lsvUEII2dYgUEIIcQaBEkKIMwiUEEKcQaCEEOIMAiWEEGcQKCGEOINACSHEme0W6Nc/u3t09MPfqHcjJl+98b3mT0u1d/sY/Mt/ODp6way6473/etr7P/5h9pc96l3ly7vfnRXfhd5bLdCv3jiq8u3fqnckJA+OGoEu1d7tY/APdbmjF35e/WWPen866/2dYdXd7l3l658ezQS6E723WqAPjr77m4s/zo/3bufrB0dzgS7V3ulj8PnRC//5ompXr5v96f3l3br3X8++4/vTu870h8es3E703maBfnm3XldfvTF7grLTqV7SNQJdqr3Tx2D6XOTH1X+nzz5+vE+9p7b4UfWfWck96l3ly7uNQHej9zYL9NPGKJ/OHm67nOlP5b/657Zup/ZOH4Ov3mhesT0YVN3p3vPM+u9X7+kPzb+ZvQe6G723WaAPZs9Ppi/0vnfJyCufT7/z923Npdr7cQxqge5f79nZlP3q/eDoe81JpN3ovcUC/fqnzdP59qzdbqd57CzV3o9jUL9w27/e/3y3ssZ+9f58+vJ91m1HeiPQrcn+CrR+/bZvvR8cHb3w9xd71rv+UYlAY9I5qFfu4gZPhgL99m/34hh8Xl/GtGe9v/7f//7u0Qv/ac961+/VDAR6lXtfDYFesZ9KvuzrM9DP775Qvf+1d70vLv6leg2/T70/rc+/8ww0Jlf3oPqypwL9tLmMft96V6neEdyj3l/erdsh0KBc2TNzvuznWfh/OJpf+bdfvevUutif3s3nr5pPHO1G720W6PyasCt3bZgvn/cuhWuuj9vpY/D1g+bjjBf71Hv+AYKZQPen97JAd6P3Ngv0yn46wZe5QHfjExp5edD56N4e9X7Qvtj43l71nqV5mb4bvbdZoNMf1N+5kp+P9WUu0KXau30MPu3W2qPeX949+qs/XHz9D83lB3vTe5ZGoLvRe5sFevHHK3qHFl/at3+Wau/yMWjuwHPU3AZgb3pX3+vZXajqV/J71LvO/ETRTvTeaoFe/PFn02P6wyv2M8mbxfvnS7V3+Bh8vnhPrK6+L72n+WP3Pqh71LtKe6Z9F3pvt0AJIWSLg0AJIcQZBEoIIc4gUEIIcQaBEkKIMwiUEEKcQaCEEOIMAiWEEGcQKCGEOINACSHEGQRKCCHOIFBCCHEGgRJCiDMIdD9yPFnO7frfrj9aa6Mnk6fud7Zz/v7hZPLUO+1/N54wUJJs7MHZ4Y1VM7MO+vm9+sCS7Q8C3Y9ECLRmTP9l/t+NZ/Ogj/7UPkLpqk9eXL1DeQf97HDNbw0JCgLdjwQI9Oxw8ifd/248mweljtCKqsf1oS3f5GDY6s2QLQkC3aec35usfH1ZmJOlJ1+nk8md7n83ns2DUrZLVx3rqePZIS/ir0QQ6D5lwwI9eKf7341n86C0QBNVpwd4JKcfj/qdIpsKAt2nINCyFAv0dLS3ZM8Og44iWSsIdJ+yLNCZHup/e/jSZHJwa/q3hy9PJpPnP2xGPH7zcPrv7V/bVKOmw7vvgZ7M3ls9eKX57zv96dWTswrz9OArT16c3D5//5npl1571AU/+/ZF96+9/ZgD30lveFW1pU03pGpH5hufNjtZvF28tF2rantIb1xCnh30YeUa0Kk83QLvgl6BINB9SkKgf9KcYnrq/rtLTjiZn3K61d/KbPibqwW6PH0669X5Oevlr0xt8m9emv392v0lcMM196Mr0MGGD/6iQaaq9bbS7khCoEvbTQv07LB5BZ8mtwJdrvzRvGJzBCoKJ+KvQBDoPiUh0Kk/Hl2cT9f505PnP7k4f28yW7sns6dNX7zbM+hxO37pLHz/dW1vesWZfuHx24OvTG0yNdOji8f3mqd7069f/7AG3DY21KYBLW/42q8vLj5+aXZyJ11tnmVST6CLl/C97SZfwrev4NPkVqBLlafmne5HPepGuy1ew1+BINB9SkqgtTaqRX2j+ULlgemibgafLK3l6b/fbv55lUD70yvOnfkGlr5Sge80u9fYpd7g9K+r9qMr0NmG5xPnM5PV5umRUgLtbzcp0PY90zR5IdBu5fbd5MX+LXaGbHEQ6D4lIdB28bYv3WevwOdruT+t+ffpEl8h0P70Baf/ldZPzabbi4NOV+5HR6Dthuc+m0k+WW2xhSVSSqD97aYEuti/NPl46WfEvPLJ8OnmyCf8yGaCQPcpCYEureWLxgvdsd2z0Z1/P14h0MH0ljP4ytQm7VPMmU26zxJT+9EVaL9As8lUtTbLpKRA+9tNCXQxP01uBbpU+bQ6Q/Xri24WmyBbHAS6T0mehW/+3l3r1avMRRaiWaz9nmaWrTKY3nIGX+l7a1mTqf3oCrQ99T2fOPtjqlrvCHRRlkAH280SaIK8OAu/RJqdaHr6tU+SO0e2Mgh0nzKSQOfvzW2fQAdPekcSaO858PgCnV3qVJ2Fby9kQqBXIQh0n1IoUPM9uPxnoMvTuwJd/sqlAk28F7hLz0CrfPzWM5VC5x9lQqBXIQh0n1Ii0NR7cPnvgS5Ptzw3y1Cgi4uBbqx4L7AvUOs90MsE2iV1dmS5mes90HKBXlx0r7PiPdArEQS6TykR6OL8cW8tn3Sss+IsfH96l7P8lb5NTjvXO91J78dQoCft07fTSXMWfrVAe6T2yW6vWX+7WWfhSwQ6/KHEWfgrEgS6TykSaHVx96Pm791bZLTXZV56HejS9AWn/5W+QNtrfNrrUc39GArUuA50tUB7pFbQp8vNXNeBFj0DbS9jWvyM4DrQKxEEuk8pEmj9+Zu3L8yP7zz/YdYnkbrTO+zeVwavZ08Hn0Qy92Mg0O4nhm5fZAjUIFV/rW8zf/9i5rXzTwbbXfFJpIUHiwQ6/fvB69N/f3yv82SXTyJdgSDQfUqZQBefQe+9Gdd8uPvaf1sp0N70Lnv5K8M3BFOfhV/ej6FAh5+Fv0SgPdL8U/7X32tNPvsg0fJ2kwKdNmk/C1/2HujZ4bzj4hIH3gK9AkGg+5RCgV48frM6L7y4RdA8Hw/vxmRZZWn6EnvpK8YZlcHdmKz9MATa3DXp1ic9ZFKgPdLsDkmvtzvy0fSLNx71t5u+nd3S3ZhMcuok0vn7N6sLQeeEagu8gr8CQaCEjJYx7wc61pbIJoNACRkt4z1v5JciXY0gUELGy1hPHHkCekWCQAkZMSM9c+QJ6BUJAiVkxFz2e+Hzwu+FvypBoISMmbPD9T9AdH6PF/BXJAiUEEKcQaCEEOIMAiWEEGcQKCGEOINACSHEGQRKCCHOIFBCCHEGgRJCiDMIlBBCnEGghBDiDAIlhBBnECghhDiDQAkhxBkESgghzvx/hoF0gM8UVRkAAAAASUVORK5CYII=" width="672" /></p>
<div class="sourceCode" id="cb86"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb86-1"><a href="#cb86-1" tabindex="-1"></a>p2 <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rddop<span class="sc">$</span>ftj, df_rddop<span class="sc">$</span>running_var2,</span>
<span id="cb86-2"><a href="#cb86-2" tabindex="-1"></a>             <span class="at">covs =</span> <span class="fu">cbind</span>(df_rddop<span class="sc">$</span>age, df_rddop<span class="sc">$</span>female, df_rddop<span class="sc">$</span>income, df_rddop<span class="sc">$</span>education, df_rddop<span class="sc">$</span>psu_rul),</span>
<span id="cb86-3"><a href="#cb86-3" tabindex="-1"></a>             <span class="at">x.lim =</span> cutoff_range, </span>
<span id="cb86-4"><a href="#cb86-4" tabindex="-1"></a>             <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb86-5"><a href="#cb86-5" tabindex="-1"></a>             <span class="at">y.lab =</span> <span class="st">&quot;Feeling thermometer to Japanese government&quot;</span>,</span>
<span id="cb86-6"><a href="#cb86-6" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Cutoff: The Gold medal confiramtion of women&#39;s Judo 48kg&quot;</span>,</span>
<span id="cb86-7"><a href="#cb86-7" tabindex="-1"></a>             <span class="at">c =</span> <span class="dv">0</span>,</span>
<span id="cb86-8"><a href="#cb86-8" tabindex="-1"></a>             <span class="at">ci =</span> <span class="dv">95</span>) </span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<div class="sourceCode" id="cb88"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb88-1"><a href="#cb88-1" tabindex="-1"></a><span class="fu">grid.arrange</span>(p1<span class="sc">$</span>rdplot, p2<span class="sc">$</span>rdplot, <span class="at">ncol =</span> <span class="dv">1</span>)</span></code></pre></div>
<p><img role="img" 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" width="672" /></p>
</div>
<div id="gradually-shifting-the-cutoff-does-not-alter-the-null-effect-of-the-opening-ceremony-for-cabinet-approval-before-opening-ceremony" class="section level3 unnumbered">
<h3 class="unnumbered">Gradually shifting the cutoff does not alter the
null effect of the opening ceremony for cabinet approval (Before opening
ceremony)</h3>
<div class="sourceCode" id="cb89"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb89-1"><a href="#cb89-1" tabindex="-1"></a>df_rddop <span class="ot">&lt;-</span> df_rddop <span class="sc">|&gt;</span></span>
<span id="cb89-2"><a href="#cb89-2" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">running_var =</span> <span class="fu">as.numeric</span>(<span class="fu">difftime</span>(StartDate, cutoff_time, <span class="at">units =</span> <span class="st">&quot;mins&quot;</span>)))</span>
<span id="cb89-3"><a href="#cb89-3" tabindex="-1"></a></span>
<span id="cb89-4"><a href="#cb89-4" tabindex="-1"></a>cutoff_diffs <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="sc">-</span><span class="dv">20</span>, <span class="sc">-</span><span class="dv">15</span>, <span class="sc">-</span><span class="dv">5</span>, <span class="sc">-</span><span class="dv">3</span>, <span class="sc">-</span><span class="dv">1</span>, <span class="dv">0</span>, <span class="dv">1</span>, <span class="dv">3</span>, <span class="dv">5</span>, <span class="dv">15</span>, <span class="dv">20</span>)</span>
<span id="cb89-5"><a href="#cb89-5" tabindex="-1"></a>titles <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;20 mins before&quot;</span>, <span class="st">&quot;15 mins before&quot;</span>, <span class="st">&quot;5 mins before&quot;</span>, <span class="st">&quot;3 mins before&quot;</span>, <span class="st">&quot;1 min before&quot;</span>,</span>
<span id="cb89-6"><a href="#cb89-6" tabindex="-1"></a>            <span class="st">&quot;At cutoff&quot;</span>, <span class="st">&quot;1 min after&quot;</span>, <span class="st">&quot;3 mins after&quot;</span>, <span class="st">&quot;5 mins after&quot;</span>, <span class="st">&quot;15 mins after&quot;</span>, <span class="st">&quot;20 mins after&quot;</span>)</span>
<span id="cb89-7"><a href="#cb89-7" tabindex="-1"></a></span>
<span id="cb89-8"><a href="#cb89-8" tabindex="-1"></a>plots <span class="ot">&lt;-</span> <span class="fu">list</span>()</span>
<span id="cb89-9"><a href="#cb89-9" tabindex="-1"></a></span>
<span id="cb89-10"><a href="#cb89-10" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="sc">:</span><span class="fu">length</span>(cutoff_diffs)) {</span>
<span id="cb89-11"><a href="#cb89-11" tabindex="-1"></a>  cutoff_range <span class="ot">&lt;-</span> <span class="fu">range</span>(df_rddop<span class="sc">$</span>running_var)</span>
<span id="cb89-12"><a href="#cb89-12" tabindex="-1"></a>  </span>
<span id="cb89-13"><a href="#cb89-13" tabindex="-1"></a>  rd <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rddop<span class="sc">$</span>cabap, df_rddop<span class="sc">$</span>running_var,</span>
<span id="cb89-14"><a href="#cb89-14" tabindex="-1"></a>               <span class="at">covs =</span> <span class="fu">cbind</span>(df_rddop<span class="sc">$</span>age, df_rddop<span class="sc">$</span>female, df_rddop<span class="sc">$</span>income, df_rddop<span class="sc">$</span>education, df_rddop<span class="sc">$</span>psu_rul),</span>
<span id="cb89-15"><a href="#cb89-15" tabindex="-1"></a>               <span class="at">x.lim =</span> cutoff_range,</span>
<span id="cb89-16"><a href="#cb89-16" tabindex="-1"></a>               <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb89-17"><a href="#cb89-17" tabindex="-1"></a>               <span class="at">y.lab =</span> <span class="st">&quot;Cabinet approval&quot;</span>,</span>
<span id="cb89-18"><a href="#cb89-18" tabindex="-1"></a>               <span class="at">title =</span> titles[i],</span>
<span id="cb89-19"><a href="#cb89-19" tabindex="-1"></a>               <span class="at">c =</span> cutoff_diffs[i])</span>
<span id="cb89-20"><a href="#cb89-20" tabindex="-1"></a>  </span>
<span id="cb89-21"><a href="#cb89-21" tabindex="-1"></a>  p <span class="ot">&lt;-</span> rd<span class="sc">$</span>rdplot <span class="sc">+</span> <span class="fu">theme_igray</span>()</span>
<span id="cb89-22"><a href="#cb89-22" tabindex="-1"></a>  </span>
<span id="cb89-23"><a href="#cb89-23" tabindex="-1"></a>  plots[[i]] <span class="ot">&lt;-</span> rd<span class="sc">$</span>rdplot</span>
<span id="cb89-24"><a href="#cb89-24" tabindex="-1"></a>}</span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" 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<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" 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<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" 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<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" 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<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" 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<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" 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<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;
## [1] &quot;Mass points detected in the running variable.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<div class="sourceCode" id="cb101"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb101-1"><a href="#cb101-1" tabindex="-1"></a>ordered_plots1 <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb101-2"><a href="#cb101-2" tabindex="-1"></a>  plots[[<span class="dv">1</span>]], plots[[<span class="dv">2</span>]], </span>
<span id="cb101-3"><a href="#cb101-3" tabindex="-1"></a>  plots[[<span class="dv">3</span>]], plots[[<span class="dv">4</span>]],  </span>
<span id="cb101-4"><a href="#cb101-4" tabindex="-1"></a>  plots[[<span class="dv">5</span>]], plots[[<span class="dv">6</span>]]</span>
<span id="cb101-5"><a href="#cb101-5" tabindex="-1"></a>)</span>
<span id="cb101-6"><a href="#cb101-6" tabindex="-1"></a></span>
<span id="cb101-7"><a href="#cb101-7" tabindex="-1"></a>ordered_plots2 <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb101-8"><a href="#cb101-8" tabindex="-1"></a>  plots[[<span class="dv">6</span>]], plots[[<span class="dv">7</span>]],  </span>
<span id="cb101-9"><a href="#cb101-9" tabindex="-1"></a>  plots[[<span class="dv">8</span>]], plots[[<span class="dv">9</span>]],</span>
<span id="cb101-10"><a href="#cb101-10" tabindex="-1"></a>  plots[[<span class="dv">10</span>]], plots[[<span class="dv">11</span>]] </span>
<span id="cb101-11"><a href="#cb101-11" tabindex="-1"></a>)</span>
<span id="cb101-12"><a href="#cb101-12" tabindex="-1"></a></span>
<span id="cb101-13"><a href="#cb101-13" tabindex="-1"></a></span>
<span id="cb101-14"><a href="#cb101-14" tabindex="-1"></a><span class="fu">grid.arrange</span>(<span class="at">grobs =</span> ordered_plots1, <span class="at">ncol =</span> <span class="dv">3</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb102"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb102-1"><a href="#cb102-1" tabindex="-1"></a><span class="fu">grid.arrange</span>(<span class="at">grobs =</span> ordered_plots1, <span class="at">ncol =</span> <span class="dv">3</span>)</span></code></pre></div>
<p><img role="img" 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" width="672" /></p>
</div>
</div>
<div id="using-other-outcome-variables" class="section level2 unnumbered">
<h2 class="unnumbered">Using other outcome variables</h2>
<div id="using-different-outcome-variables-the-null-result-for-the-first-gold-medal-is-maintained-rdd" class="section level3 unnumbered">
<h3 class="unnumbered">Using different outcome variables, the null
result for the first gold medal is maintained (RDD)</h3>
<div class="sourceCode" id="cb103"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb103-1"><a href="#cb103-1" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> <span class="fu">cbind</span>(df_rdd48<span class="sc">$</span>age, df_rdd48<span class="sc">$</span>female, df_rdd48<span class="sc">$</span>income, df_rdd48<span class="sc">$</span>education, df_rdd48<span class="sc">$</span>psu_rul)</span>
<span id="cb103-2"><a href="#cb103-2" tabindex="-1"></a></span>
<span id="cb103-3"><a href="#cb103-3" tabindex="-1"></a>results_judo48p <span class="ot">&lt;-</span> <span class="fu">list</span>()</span>
<span id="cb103-4"><a href="#cb103-4" tabindex="-1"></a>outcome_vars    <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;ftl&quot;</span>, <span class="st">&quot;ftki&quot;</span>, <span class="st">&quot;busi&quot;</span>, <span class="st">&quot;liv&quot;</span>)</span>
<span id="cb103-5"><a href="#cb103-5" tabindex="-1"></a>outcome_prefix  <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;fl&quot;</span>,  <span class="st">&quot;fk&quot;</span>,   <span class="st">&quot;bs&quot;</span>,   <span class="st">&quot;lv&quot;</span>)</span>
<span id="cb103-6"><a href="#cb103-6" tabindex="-1"></a></span>
<span id="cb103-7"><a href="#cb103-7" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">seq_along</span>(outcome_vars)) {</span>
<span id="cb103-8"><a href="#cb103-8" tabindex="-1"></a>  outcome_var <span class="ot">&lt;-</span> outcome_vars[i]</span>
<span id="cb103-9"><a href="#cb103-9" tabindex="-1"></a>  prefix      <span class="ot">&lt;-</span> outcome_prefix[i]</span>
<span id="cb103-10"><a href="#cb103-10" tabindex="-1"></a>  </span>
<span id="cb103-11"><a href="#cb103-11" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res2&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">rdrobust</span>(df_rdd48[[outcome_var]],</span>
<span id="cb103-12"><a href="#cb103-12" tabindex="-1"></a>                                                        df_rdd48<span class="sc">$</span>running_var2, </span>
<span id="cb103-13"><a href="#cb103-13" tabindex="-1"></a>                                                        <span class="at">covs =</span> covariates,</span>
<span id="cb103-14"><a href="#cb103-14" tabindex="-1"></a>                                                        <span class="at">bwselect =</span> <span class="st">&quot;msesum&quot;</span>, <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb103-15"><a href="#cb103-15" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res3&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">rdrobust</span>(df_rdd48[[outcome_var]], </span>
<span id="cb103-16"><a href="#cb103-16" tabindex="-1"></a>                                                        df_rdd48<span class="sc">$</span>running_var2, </span>
<span id="cb103-17"><a href="#cb103-17" tabindex="-1"></a>                                                        <span class="at">covs =</span> covariates, </span>
<span id="cb103-18"><a href="#cb103-18" tabindex="-1"></a>                                                        <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb103-19"><a href="#cb103-19" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res4&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">rdrobust</span>(df_rdd48[[outcome_var]], </span>
<span id="cb103-20"><a href="#cb103-20" tabindex="-1"></a>                                                        df_rdd48<span class="sc">$</span>running_var2, </span>
<span id="cb103-21"><a href="#cb103-21" tabindex="-1"></a>                                                        <span class="at">covs =</span> covariates, </span>
<span id="cb103-22"><a href="#cb103-22" tabindex="-1"></a>                                                        <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;uni&quot;</span>, <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb103-23"><a href="#cb103-23" tabindex="-1"></a>  results_judo48p[[<span class="fu">paste0</span>(<span class="st">&quot;res5&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">rdrobust</span>(df_rdd48[[outcome_var]], </span>
<span id="cb103-24"><a href="#cb103-24" tabindex="-1"></a>                                                        df_rdd48<span class="sc">$</span>running_var2, </span>
<span id="cb103-25"><a href="#cb103-25" tabindex="-1"></a>                                                        <span class="at">covs =</span> covariates, </span>
<span id="cb103-26"><a href="#cb103-26" tabindex="-1"></a>                                                        <span class="at">bwselect =</span> <span class="st">&quot;cerrd&quot;</span>, <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb103-27"><a href="#cb103-27" tabindex="-1"></a>}</span>
<span id="cb103-28"><a href="#cb103-28" tabindex="-1"></a></span>
<span id="cb103-29"><a href="#cb103-29" tabindex="-1"></a><span class="fu">source</span>(<span class="st">&quot;process_results_rdd.R&quot;</span>)</span>
<span id="cb103-30"><a href="#cb103-30" tabindex="-1"></a></span>
<span id="cb103-31"><a href="#cb103-31" tabindex="-1"></a>result_sets <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb103-32"><a href="#cb103-32" tabindex="-1"></a>  <span class="st">&quot;Feeling thermometer to the LDP&quot;</span> <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;res2fl&quot;</span>,<span class="st">&quot;res3fl&quot;</span>,<span class="st">&quot;res4fl&quot;</span>,<span class="st">&quot;res5fl&quot;</span>),</span>
<span id="cb103-33"><a href="#cb103-33" tabindex="-1"></a>  <span class="st">&quot;Feeling thermometer to the PM&quot;</span>  <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;res2fk&quot;</span>,<span class="st">&quot;res3fk&quot;</span>,<span class="st">&quot;res4fk&quot;</span>,<span class="st">&quot;res5fk&quot;</span>),</span>
<span id="cb103-34"><a href="#cb103-34" tabindex="-1"></a>  <span class="st">&quot;Sociotropic economic evaluation&quot;</span><span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;res2bs&quot;</span>,<span class="st">&quot;res3bs&quot;</span>,<span class="st">&quot;res4bs&quot;</span>,<span class="st">&quot;res5bs&quot;</span>),</span>
<span id="cb103-35"><a href="#cb103-35" tabindex="-1"></a>  <span class="st">&quot;Egotropic economic evaluation&quot;</span>  <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;res2lv&quot;</span>,<span class="st">&quot;res3lv&quot;</span>,<span class="st">&quot;res4lv&quot;</span>,<span class="st">&quot;res5lv&quot;</span>)</span>
<span id="cb103-36"><a href="#cb103-36" tabindex="-1"></a>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
The first gold medal
</td>
<td style="text-align:center;">
4.396
</td>
<td style="text-align:center;">
4.321
</td>
<td style="text-align:center;">
-0.320
</td>
<td style="text-align:center;">
7.068
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-10.839, 19.630]
</td>
<td style="text-align:center;">
[-10.821, 19.463]
</td>
<td style="text-align:center;">
[-12.977, 12.336]
</td>
<td style="text-align:center;">
[-12.366, 26.503]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.572
</td>
<td style="text-align:center;">
0.576
</td>
<td style="text-align:center;">
0.960
</td>
<td style="text-align:center;">
0.476
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
40.735
</td>
<td style="text-align:center;">
41.311
</td>
<td style="text-align:center;">
49.241
</td>
<td style="text-align:center;">
29.69
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
76.39
</td>
<td style="text-align:center;">
76.383
</td>
<td style="text-align:center;">
105.158
</td>
<td style="text-align:center;">
76.383
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<span style="font-style: italic;">Note: </span>
</td>
</tr>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01. Standard errors in
parentheses.
</td>
</tr>
</tfoot>
</table></li>
</ol></li>
</ol></li>
</ol></li>
</ol>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
The first gold medal
</td>
<td style="text-align:center;">
3.498
</td>
<td style="text-align:center;">
3.525
</td>
<td style="text-align:center;">
2.335
</td>
<td style="text-align:center;">
5.137
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-10.858, 17.855]
</td>
<td style="text-align:center;">
[-10.843, 17.892]
</td>
<td style="text-align:center;">
[-11.454, 16.124]
</td>
<td style="text-align:center;">
[-10.310, 20.583]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.633
</td>
<td style="text-align:center;">
0.631
</td>
<td style="text-align:center;">
0.740
</td>
<td style="text-align:center;">
0.515
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
57.981
</td>
<td style="text-align:center;">
57.702
</td>
<td style="text-align:center;">
45.406
</td>
<td style="text-align:center;">
41.47
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
90.139
</td>
<td style="text-align:center;">
90.131
</td>
<td style="text-align:center;">
111.931
</td>
<td style="text-align:center;">
90.131
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<span style="font-style: italic;">Note: </span>
</td>
</tr>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01. Standard errors in
parentheses.
</td>
</tr>
</tfoot>
</table></li>
</ol></li>
</ol></li>
</ol></li>
</ol>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
The first gold medal
</td>
<td style="text-align:center;">
-0.162
</td>
<td style="text-align:center;">
-0.162
</td>
<td style="text-align:center;">
-0.215
</td>
<td style="text-align:center;">
-0.146
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-0.819, 0.495]
</td>
<td style="text-align:center;">
[-0.818, 0.494]
</td>
<td style="text-align:center;">
[-0.806, 0.375]
</td>
<td style="text-align:center;">
[-0.854, 0.563]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.629
</td>
<td style="text-align:center;">
0.628
</td>
<td style="text-align:center;">
0.475
</td>
<td style="text-align:center;">
0.687
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
79.58
</td>
<td style="text-align:center;">
79.771
</td>
<td style="text-align:center;">
63.268
</td>
<td style="text-align:center;">
57.33
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
112.905
</td>
<td style="text-align:center;">
112.904
</td>
<td style="text-align:center;">
103.754
</td>
<td style="text-align:center;">
112.904
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<span style="font-style: italic;">Note: </span>
</td>
</tr>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01. Standard errors in
parentheses.
</td>
</tr>
</tfoot>
</table></li>
</ol></li>
</ol></li>
</ol></li>
</ol>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
The first gold medal
</td>
<td style="text-align:center;">
-0.482
</td>
<td style="text-align:center;">
-0.482
</td>
<td style="text-align:center;">
-0.488
</td>
<td style="text-align:center;">
-0.497
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-1.007, 0.043]
</td>
<td style="text-align:center;">
[-1.009, 0.045]
</td>
<td style="text-align:center;">
[-1.030, 0.055]
</td>
<td style="text-align:center;">
[-1.033, 0.039]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.072
</td>
<td style="text-align:center;">
0.073
</td>
<td style="text-align:center;">
0.078
</td>
<td style="text-align:center;">
0.069
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
<td style="text-align:center;">
740
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
56.812
</td>
<td style="text-align:center;">
51.329
</td>
<td style="text-align:center;">
55.146
</td>
<td style="text-align:center;">
36.889
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
137.786
</td>
<td style="text-align:center;">
137.782
</td>
<td style="text-align:center;">
112.098
</td>
<td style="text-align:center;">
137.782
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<span style="font-style: italic;">Note: </span>
</td>
</tr>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01. Standard errors in
parentheses.
</td>
</tr>
</tfoot>
</table></li>
</ol></li>
</ol></li>
</ol></li>
</ol>
</div>
<div id="using-different-outcome-variables-the-null-result-for-the-second-gold-medal-is-maintained-rdd" class="section level3 unnumbered">
<h3 class="unnumbered">Using different outcome variables, the null
result for the second gold medal is maintained (RDD)</h3>
<div class="sourceCode" id="cb104"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb104-1"><a href="#cb104-1" tabindex="-1"></a>covariates <span class="ot">&lt;-</span> <span class="fu">cbind</span>(df_rdd66<span class="sc">$</span>age, df_rdd66<span class="sc">$</span>female, df_rdd66<span class="sc">$</span>income, df_rdd66<span class="sc">$</span>education, df_rdd66<span class="sc">$</span>psu_rul)</span>
<span id="cb104-2"><a href="#cb104-2" tabindex="-1"></a></span>
<span id="cb104-3"><a href="#cb104-3" tabindex="-1"></a>results_judo66p <span class="ot">&lt;-</span> <span class="fu">list</span>()</span>
<span id="cb104-4"><a href="#cb104-4" tabindex="-1"></a>outcome_vars    <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;ftl&quot;</span>, <span class="st">&quot;ftki&quot;</span>, <span class="st">&quot;busi&quot;</span>, <span class="st">&quot;liv&quot;</span>)</span>
<span id="cb104-5"><a href="#cb104-5" tabindex="-1"></a>outcome_prefix  <span class="ot">&lt;-</span> <span class="fu">c</span>(<span class="st">&quot;fl&quot;</span>,  <span class="st">&quot;fk&quot;</span>,   <span class="st">&quot;bs&quot;</span>,   <span class="st">&quot;lv&quot;</span>)</span>
<span id="cb104-6"><a href="#cb104-6" tabindex="-1"></a></span>
<span id="cb104-7"><a href="#cb104-7" tabindex="-1"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="fu">seq_along</span>(outcome_vars)) {</span>
<span id="cb104-8"><a href="#cb104-8" tabindex="-1"></a>  outcome_var <span class="ot">&lt;-</span> outcome_vars[i]</span>
<span id="cb104-9"><a href="#cb104-9" tabindex="-1"></a>  prefix      <span class="ot">&lt;-</span> outcome_prefix[i]</span>
<span id="cb104-10"><a href="#cb104-10" tabindex="-1"></a>  </span>
<span id="cb104-11"><a href="#cb104-11" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res2&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">rdrobust</span>(df_rdd66[[outcome_var]],</span>
<span id="cb104-12"><a href="#cb104-12" tabindex="-1"></a>                                                        df_rdd66<span class="sc">$</span>running_var2, </span>
<span id="cb104-13"><a href="#cb104-13" tabindex="-1"></a>                                                        <span class="at">covs =</span> covariates,</span>
<span id="cb104-14"><a href="#cb104-14" tabindex="-1"></a>                                                        <span class="at">bwselect =</span> <span class="st">&quot;msesum&quot;</span>, <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb104-15"><a href="#cb104-15" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res3&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">rdrobust</span>(df_rdd66[[outcome_var]], </span>
<span id="cb104-16"><a href="#cb104-16" tabindex="-1"></a>                                                        df_rdd66<span class="sc">$</span>running_var2, </span>
<span id="cb104-17"><a href="#cb104-17" tabindex="-1"></a>                                                        <span class="at">covs =</span> covariates, </span>
<span id="cb104-18"><a href="#cb104-18" tabindex="-1"></a>                                                        <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;triangular&quot;</span>, <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb104-19"><a href="#cb104-19" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res4&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">rdrobust</span>(df_rdd66[[outcome_var]], </span>
<span id="cb104-20"><a href="#cb104-20" tabindex="-1"></a>                                                        df_rdd66<span class="sc">$</span>running_var2, </span>
<span id="cb104-21"><a href="#cb104-21" tabindex="-1"></a>                                                        <span class="at">covs =</span> covariates, </span>
<span id="cb104-22"><a href="#cb104-22" tabindex="-1"></a>                                                        <span class="at">bwselect =</span> <span class="st">&quot;mserd&quot;</span>, <span class="at">kernel =</span> <span class="st">&quot;uni&quot;</span>, <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb104-23"><a href="#cb104-23" tabindex="-1"></a>  results_judo66p[[<span class="fu">paste0</span>(<span class="st">&quot;res5&quot;</span>, prefix)]] <span class="ot">&lt;-</span> <span class="fu">rdrobust</span>(df_rdd66[[outcome_var]], </span>
<span id="cb104-24"><a href="#cb104-24" tabindex="-1"></a>                                                        df_rdd66<span class="sc">$</span>running_var2, </span>
<span id="cb104-25"><a href="#cb104-25" tabindex="-1"></a>                                                        <span class="at">covs =</span> covariates, </span>
<span id="cb104-26"><a href="#cb104-26" tabindex="-1"></a>                                                        <span class="at">bwselect =</span> <span class="st">&quot;cerrd&quot;</span>, <span class="at">all =</span> <span class="cn">TRUE</span>)</span>
<span id="cb104-27"><a href="#cb104-27" tabindex="-1"></a>}</span>
<span id="cb104-28"><a href="#cb104-28" tabindex="-1"></a></span>
<span id="cb104-29"><a href="#cb104-29" tabindex="-1"></a><span class="fu">source</span>(<span class="st">&quot;process_results_rdd.R&quot;</span>)</span>
<span id="cb104-30"><a href="#cb104-30" tabindex="-1"></a></span>
<span id="cb104-31"><a href="#cb104-31" tabindex="-1"></a>result_sets <span class="ot">&lt;-</span> <span class="fu">list</span>(</span>
<span id="cb104-32"><a href="#cb104-32" tabindex="-1"></a>  <span class="st">&quot;Feeling thermometer to the LDP&quot;</span> <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;res2fl&quot;</span>,<span class="st">&quot;res3fl&quot;</span>,<span class="st">&quot;res4fl&quot;</span>,<span class="st">&quot;res5fl&quot;</span>),</span>
<span id="cb104-33"><a href="#cb104-33" tabindex="-1"></a>  <span class="st">&quot;Feeling thermometer to the PM&quot;</span>  <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;res2fk&quot;</span>,<span class="st">&quot;res3fk&quot;</span>,<span class="st">&quot;res4fk&quot;</span>,<span class="st">&quot;res5fk&quot;</span>),</span>
<span id="cb104-34"><a href="#cb104-34" tabindex="-1"></a>  <span class="st">&quot;Sociotropic economic evaluation&quot;</span><span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;res2bs&quot;</span>,<span class="st">&quot;res3bs&quot;</span>,<span class="st">&quot;res4bs&quot;</span>,<span class="st">&quot;res5bs&quot;</span>),</span>
<span id="cb104-35"><a href="#cb104-35" tabindex="-1"></a>  <span class="st">&quot;Egotropic economic evaluation&quot;</span>  <span class="ot">=</span> <span class="fu">c</span>(<span class="st">&quot;res2lv&quot;</span>,<span class="st">&quot;res3lv&quot;</span>,<span class="st">&quot;res4lv&quot;</span>,<span class="st">&quot;res5lv&quot;</span>)</span>
<span id="cb104-36"><a href="#cb104-36" tabindex="-1"></a>)</span></code></pre></div>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
The first gold medal
</td>
<td style="text-align:center;">
-3.336
</td>
<td style="text-align:center;">
-3.308
</td>
<td style="text-align:center;">
1.848
</td>
<td style="text-align:center;">
-3.400
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-16.027, 9.354]
</td>
<td style="text-align:center;">
[-15.701, 9.085]
</td>
<td style="text-align:center;">
[-14.873, 18.568]
</td>
<td style="text-align:center;">
[-17.775, 10.975]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.606
</td>
<td style="text-align:center;">
0.601
</td>
<td style="text-align:center;">
0.829
</td>
<td style="text-align:center;">
0.643
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
3.938
</td>
<td style="text-align:center;">
4.172
</td>
<td style="text-align:center;">
2.32
</td>
<td style="text-align:center;">
3.004
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
5.817
</td>
<td style="text-align:center;">
5.935
</td>
<td style="text-align:center;">
4.592
</td>
<td style="text-align:center;">
5.935
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<span style="font-style: italic;">Note: </span>
</td>
</tr>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01. Standard errors in
parentheses.
</td>
</tr>
</tfoot>
</table></li>
</ol></li>
</ol></li>
</ol></li>
</ol>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
The first gold medal
</td>
<td style="text-align:center;">
0.338
</td>
<td style="text-align:center;">
0.358
</td>
<td style="text-align:center;">
0.991
</td>
<td style="text-align:center;">
0.101
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-14.370, 15.045]
</td>
<td style="text-align:center;">
[-14.337, 15.054]
</td>
<td style="text-align:center;">
[-13.570, 15.552]
</td>
<td style="text-align:center;">
[-19.122, 19.324]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.964
</td>
<td style="text-align:center;">
0.962
</td>
<td style="text-align:center;">
0.894
</td>
<td style="text-align:center;">
0.992
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
3.433
</td>
<td style="text-align:center;">
3.44
</td>
<td style="text-align:center;">
2.81
</td>
<td style="text-align:center;">
2.477
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
4.98
</td>
<td style="text-align:center;">
4.987
</td>
<td style="text-align:center;">
5.005
</td>
<td style="text-align:center;">
4.987
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<span style="font-style: italic;">Note: </span>
</td>
</tr>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01. Standard errors in
parentheses.
</td>
</tr>
</tfoot>
</table></li>
</ol></li>
</ol></li>
</ol></li>
</ol>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
The first gold medal
</td>
<td style="text-align:center;">
-0.035
</td>
<td style="text-align:center;">
0.051
</td>
<td style="text-align:center;">
-4.388
</td>
<td style="text-align:center;">
0.010
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-0.652, 0.583]
</td>
<td style="text-align:center;">
[-0.505, 0.607]
</td>
<td style="text-align:center;">
[-33.791, 25.014]
</td>
<td style="text-align:center;">
[-0.723, 0.743]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.913
</td>
<td style="text-align:center;">
0.857
</td>
<td style="text-align:center;">
0.770
</td>
<td style="text-align:center;">
0.979
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
2.8
</td>
<td style="text-align:center;">
3.256
</td>
<td style="text-align:center;">
1.433
</td>
<td style="text-align:center;">
2.344
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
4.671
</td>
<td style="text-align:center;">
4.848
</td>
<td style="text-align:center;">
3.244
</td>
<td style="text-align:center;">
4.848
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<span style="font-style: italic;">Note: </span>
</td>
</tr>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01. Standard errors in
parentheses.
</td>
</tr>
</tfoot>
</table></li>
</ol></li>
</ol></li>
</ol></li>
</ol>
<table class="table table-striped table-hover table-condensed lightable-classic" style="width: auto !important; margin-left: auto; margin-right: auto; font-family: Times; width: auto !important; margin-left: auto; margin-right: auto;border-bottom: 0;">
<thead>
<tr>
<th style="text-align:left;font-weight: bold;">
</th>
<th style="text-align:center;font-weight: bold;">
<ol style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="2" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="3" style="list-style-type: decimal">
<li></th>
<th style="text-align:center;font-weight: bold;">
<ol start="4" style="list-style-type: decimal">
<li></th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
The first gold medal
</td>
<td style="text-align:center;">
0.217
</td>
<td style="text-align:center;">
0.251
</td>
<td style="text-align:center;">
1.918
</td>
<td style="text-align:center;">
0.130
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust 95% CI
</td>
<td style="text-align:center;">
[-0.339, 0.773]
</td>
<td style="text-align:center;">
[-0.288, 0.791]
</td>
<td style="text-align:center;">
[-28.799, 32.635]
</td>
<td style="text-align:center;">
[-0.582, 0.843]
</td>
</tr>
<tr>
<td style="text-align:left;">
Robust <span class="math inline">\(p\)</span>-value
</td>
<td style="text-align:center;">
0.444
</td>
<td style="text-align:center;">
0.362
</td>
<td style="text-align:center;">
0.903
</td>
<td style="text-align:center;">
0.720
</td>
</tr>
<tr>
<td style="text-align:left;">
Observations
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
<td style="text-align:center;">
712
</td>
</tr>
<tr>
<td style="text-align:left;">
Kernel type
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Triangular
</td>
<td style="text-align:center;">
Uniform
</td>
<td style="text-align:center;">
Triangular
</td>
</tr>
<tr>
<td style="text-align:left;">
BW type
</td>
<td style="text-align:center;">
msesum
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
mserd
</td>
<td style="text-align:center;">
cerrd
</td>
</tr>
<tr>
<td style="text-align:left;">
Order loc. poly.(<span class="math inline">\(p\)</span>)
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
<td style="text-align:center;">
1
</td>
</tr>
<tr>
<td style="text-align:left;">
Order bias (<span class="math inline">\(q\)</span>)
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
<td style="text-align:center;">
2
</td>
</tr>
<tr>
<td style="text-align:left;">
BW loc. poly.(<span class="math inline">\(h\)</span>)
</td>
<td style="text-align:center;">
3.372
</td>
<td style="text-align:center;">
3.661
</td>
<td style="text-align:center;">
1.688
</td>
<td style="text-align:center;">
2.636
</td>
</tr>
<tr>
<td style="text-align:left;">
BW bias (<span class="math inline">\(b\)</span>)
</td>
<td style="text-align:center;">
5.264
</td>
<td style="text-align:center;">
5.639
</td>
<td style="text-align:center;">
3.684
</td>
<td style="text-align:center;">
5.639
</td>
</tr>
</tbody>
<tfoot>
<tr>
<td style="padding: 0; " colspan="100%">
<span style="font-style: italic;">Note: </span>
</td>
</tr>
<tr>
<td style="padding: 0; " colspan="100%">
<sup></sup> * p&lt;0.10, ** p&lt;0.05, *** p&lt;0.01. Standard errors in
parentheses.
</td>
</tr>
</tfoot>
</table></li>
</ol></li>
</ol></li>
</ol></li>
</ol>
</div>
<div id="even-when-we-use-other-outcome-variables-the-sate-for-the-first-gold-medal-is-still-null-rdd" class="section level3 unnumbered">
<h3 class="unnumbered">Even when we use other outcome variables, the
SATE for the first gold medal is still null (RDD)</h3>
<div class="sourceCode" id="cb105"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb105-1"><a href="#cb105-1" tabindex="-1"></a>p1 <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rdd48<span class="sc">$</span>ftl, df_rdd48<span class="sc">$</span>running_var2,</span>
<span id="cb105-2"><a href="#cb105-2" tabindex="-1"></a>             <span class="at">covs =</span> <span class="fu">cbind</span>(df_rdd48<span class="sc">$</span>age, df_rdd48<span class="sc">$</span>female, df_rdd48<span class="sc">$</span>income, df_rdd48<span class="sc">$</span>education, df_rdd48<span class="sc">$</span>psu_rul),</span>
<span id="cb105-3"><a href="#cb105-3" tabindex="-1"></a>             <span class="at">x.lim =</span> cutoff_range2, </span>
<span id="cb105-4"><a href="#cb105-4" tabindex="-1"></a>             <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb105-5"><a href="#cb105-5" tabindex="-1"></a>             <span class="at">y.lab =</span> <span class="st">&quot;Feeling thermometer to the LDP&quot;</span>,</span>
<span id="cb105-6"><a href="#cb105-6" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Cutoff: The Gold medal confiramtion of women&#39;s judo 48kg&quot;</span>,</span>
<span id="cb105-7"><a href="#cb105-7" tabindex="-1"></a>             <span class="at">c =</span> <span class="dv">0</span>) </span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
<div class="sourceCode" id="cb107"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb107-1"><a href="#cb107-1" tabindex="-1"></a>p2 <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rdd48<span class="sc">$</span>ftki, df_rdd48<span class="sc">$</span>running_var2,</span>
<span id="cb107-2"><a href="#cb107-2" tabindex="-1"></a>             <span class="at">covs =</span> <span class="fu">cbind</span>(df_rdd48<span class="sc">$</span>age, df_rdd48<span class="sc">$</span>female, df_rdd48<span class="sc">$</span>income, df_rdd48<span class="sc">$</span>education, df_rdd48<span class="sc">$</span>psu_rul),</span>
<span id="cb107-3"><a href="#cb107-3" tabindex="-1"></a>             <span class="at">x.lim =</span> cutoff_range2, </span>
<span id="cb107-4"><a href="#cb107-4" tabindex="-1"></a>             <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb107-5"><a href="#cb107-5" tabindex="-1"></a>             <span class="at">y.lab =</span> <span class="st">&quot;Feeling thermometer to Prime Minisiter Kishida&quot;</span>,</span>
<span id="cb107-6"><a href="#cb107-6" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Cutoff: The Gold medal confiramtion of women&#39;s judo 48kg&quot;</span>,</span>
<span id="cb107-7"><a href="#cb107-7" tabindex="-1"></a>             <span class="at">c =</span> <span class="dv">0</span>) </span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<div class="sourceCode" id="cb109"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb109-1"><a href="#cb109-1" tabindex="-1"></a>p3 <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rdd48<span class="sc">$</span>busi, df_rdd48<span class="sc">$</span>running_var2,</span>
<span id="cb109-2"><a href="#cb109-2" tabindex="-1"></a>             <span class="at">covs =</span> <span class="fu">cbind</span>(df_rdd48<span class="sc">$</span>age, df_rdd48<span class="sc">$</span>female, df_rdd48<span class="sc">$</span>income, df_rdd48<span class="sc">$</span>education, df_rdd48<span class="sc">$</span>psu_rul),</span>
<span id="cb109-3"><a href="#cb109-3" tabindex="-1"></a>             <span class="at">x.lim =</span> cutoff_range2, </span>
<span id="cb109-4"><a href="#cb109-4" tabindex="-1"></a>             <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb109-5"><a href="#cb109-5" tabindex="-1"></a>             <span class="at">y.lab =</span> <span class="st">&quot;Sociotropic economic evaluation&quot;</span>,</span>
<span id="cb109-6"><a href="#cb109-6" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Cutoff: The Gold medal confiramtion of women&#39;s judo 48kg&quot;</span>,</span>
<span id="cb109-7"><a href="#cb109-7" tabindex="-1"></a>             <span class="at">c =</span> <span class="dv">0</span>) </span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<div class="sourceCode" id="cb111"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb111-1"><a href="#cb111-1" tabindex="-1"></a>p4 <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rdd48<span class="sc">$</span>liv, df_rdd48<span class="sc">$</span>running_var2,</span>
<span id="cb111-2"><a href="#cb111-2" tabindex="-1"></a>             <span class="at">covs =</span> <span class="fu">cbind</span>(df_rdd48<span class="sc">$</span>age, df_rdd48<span class="sc">$</span>female, df_rdd48<span class="sc">$</span>income, df_rdd48<span class="sc">$</span>education, df_rdd48<span class="sc">$</span>psu_rul),</span>
<span id="cb111-3"><a href="#cb111-3" tabindex="-1"></a>             <span class="at">x.lim =</span> cutoff_range2, </span>
<span id="cb111-4"><a href="#cb111-4" tabindex="-1"></a>             <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb111-5"><a href="#cb111-5" tabindex="-1"></a>             <span class="at">y.lab =</span> <span class="st">&quot;Egotropic economic evaluation&quot;</span>,</span>
<span id="cb111-6"><a href="#cb111-6" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Cutoff: The Gold medal confiramtion of women&#39;s judo 48kg&quot;</span>,</span>
<span id="cb111-7"><a href="#cb111-7" tabindex="-1"></a>             <span class="at">c =</span> <span class="dv">0</span>) </span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<div class="sourceCode" id="cb113"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb113-1"><a href="#cb113-1" tabindex="-1"></a><span class="fu">grid.arrange</span>(p1<span class="sc">$</span>rdplot, p2<span class="sc">$</span>rdplot,</span>
<span id="cb113-2"><a href="#cb113-2" tabindex="-1"></a>             p3<span class="sc">$</span>rdplot, p4<span class="sc">$</span>rdplot,<span class="at">ncol =</span> <span class="dv">2</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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" width="672" /></p>
</div>
<div id="even-when-we-use-other-outcome-variables-the-sate-for-the-second-gold-medal-is-still-null-rdd" class="section level3 unnumbered">
<h3 class="unnumbered">Even when we use other outcome variables, the
SATE for the second gold medal is still null (RDD)</h3>
<div class="sourceCode" id="cb114"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb114-1"><a href="#cb114-1" tabindex="-1"></a>p1 <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rdd66<span class="sc">$</span>ftl, df_rdd66<span class="sc">$</span>running_var2,</span>
<span id="cb114-2"><a href="#cb114-2" tabindex="-1"></a>             <span class="at">covs =</span> <span class="fu">cbind</span>(df_rdd66<span class="sc">$</span>age, df_rdd66<span class="sc">$</span>female, df_rdd66<span class="sc">$</span>income, df_rdd66<span class="sc">$</span>education, df_rdd66<span class="sc">$</span>psu_rul),</span>
<span id="cb114-3"><a href="#cb114-3" tabindex="-1"></a>             <span class="at">x.lim =</span> cutoff_range2, </span>
<span id="cb114-4"><a href="#cb114-4" tabindex="-1"></a>             <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb114-5"><a href="#cb114-5" tabindex="-1"></a>             <span class="at">y.lab =</span> <span class="st">&quot;Feeling thermometer to the LDP&quot;</span>,</span>
<span id="cb114-6"><a href="#cb114-6" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Cutoff: The Gold medal confiramtion of women&#39;s judo 66kg&quot;</span>,</span>
<span id="cb114-7"><a href="#cb114-7" tabindex="-1"></a>             <span class="at">c =</span> <span class="dv">0</span>) </span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABUAAAAPACAMAAADDuCPrAAABelBMVEUAAAAAADoAAGYAAIsAOjoAOmYAOpAAZpAAZrYzMzM6AAA6OgA6Ojo6OmY6OpA6Zjo6ZmY6ZpA6ZrY6kJA6kLY6kNtNTU1NTW5NTY5Nbm5Nbo5NbqtNjshmAABmADpmOgBmOjpmZgBmZjpmZmZmZpBmkGZmkJBmkLZmkNtmtrZmtttmtv9uTU1ubk1ubm5ubo5ujqtujshuq+SOTU2Obk2Obm6Oq6uOq8iOq+SOyOSOyP+QOgCQOjqQZgCQZjqQZmaQkDqQkGaQkLaQtpCQtraQttuQtv+Q27aQ29uQ2/+rbk2rbm6rjm6ryOSr5P+2ZgC2Zjq2Zma2kDq2kGa2kJC2tpC2tra2ttu225C229u22/+2/7a2///Ijk3Ijm7Iq27I5P/I///bkDrbkGbbtmbbtpDbtrbb27bb29vb2//b/7bb///kq27kyI7kyKvk///r6+v/AAD/tmb/yI7/25D/27b/29v/5Kv/5Mj//7b//8j//9v//+T///+z7ASEAAAACXBIWXMAAB2HAAAdhwGP5fFlAAAgAElEQVR4nO2d+4McVX7da2hJlgdpI8iq2QBaZIkFGykJXmwnXq1R1jFJIHiHXWwIBG3YWbQgSzbSaPRi1P97uqrr/ei+31u3vre/fc75QZrpmapTp/r0Z+pxqypZUBRFUV5KYi8ARVGUVRGgFEVRniJAKYqiPEWAUhRFeYoApSiK8hQBSlEU5SkClKIoylMEKEVRlKcIUIqiKE8RoBRFUZ4iQCmKojxFgFIURXmKAKUoivIUAUpRFOUpApSiKMpT0wP05Pbl/SRJTr30xYiZfHMuncX/aHzV0lHS1bXF4iBJLsr9Hv3qXLbQFz64O/xLzy4lex82X/Jz69fgvHp8vbVanf8QcI7hl67nzdbQslAbgyzDvvDV5lm51uLR+8vW7b305fBLhwELRgXQ1AA9+aTC2ekv1/3m7cvDbV3NJK1z9VVbAQF6+41qFns/HkToDgA0X53/M8Qc8zcw/NKFwrFQ6gA9eb8o3cXBlwjQLdPEAD3er/Ns79rgL57cWNPWnI3LqlZfDf3SeIDWmZ9xf+gTYh+gxer8v+PnWL6B4ZfOgVBTSBugyzVYL27/SwTolmlagGb8PP3BneWX375fK0ZXaz92y9bsfdD6ap1nvdNipK1Kez5b6Mefvrj8+szANqh9gDqsTleF3HPPFXDpfOQCUEc51WL5S0m6q357v/yj0X2JAN0yTQrQ5Ycq2Xun+O7RpTVbE2s/f8senW1/NaixAE1LWzvYcJgMzsA+QB1Wp6smAGjApfPRskiaAE03Nla/lH5sLg68RIBumSYF6EHzb3jVh642AfRi+6tBjQRoupCNTc7DQezvBEBDLe40AI0JC8fdcxe5JDmoeneYf9nzEgG6ZZoSoB1gHop2hytpAvSgzcv0j3//kQcCtKbdA+jJjcG2SuWQpF6zvMI9LxGg26YpAdrZdju+/Obn6f+1Ph1mu2nFuab8I/jt5drQjfI81Av/p/zqq8Zf56b6AJqNBTn1dvX7PeNFyqnbDT089fad/MvGgtWYcXL78vL1l+/2fFIO0t/J7M5nx/PS30zOV0O6OkvSN6/b72eDqvLl6GXVSXa49nztmGFzYdPZXWusiGrFflXMcfluXDv5ZLnrev7Dru26KLU3sLZ0m5agpYG3vXozD0qgpD/NvzwsdvQ7bqIV37NwB8URhEfvp+u2byBeuZXaqXT2nvS8la2lLLVM1O5zz0s1gKbHllaHx07S0V797aOm1oQATc/G9B/DWg/Q6tTjy2l9AgD0m3zC4tBmdZ795Z6CDu63tRasQsWjN4rF7wfoYT7Zxcr54tCS9MzrUTmqajWMoQ+gfyjWTbFS2gubEaJwyyYfAOhB8Qtt23VR+gC6eQnWrd0+gFZoOkrqOyXX+t1EK75n4Q5Xv1wbldEZiLcWoD1vZWcpe7Kte6kCaMXPde2jptaEAC0PfHe0FqDpZIVSGowH6LnG/JqDQ9oNHZxtd8FKVFRjtV74sz6A/qqc7J2D4qs2Y4ol6ZlX3TebrAegh9WvnLnbnmj1SmdF9AP0XPHzju26KD0AdViCdWu3D6DVW3tQD1rs7rbdRCt+eOEOql/t/HFdB9ANb2VrBWRkXG2yFpunPS+VAD0s/6xV8+xpHzW1JgRobTerpZ62VVAohm58m/5hvdj6fa9joKv5ZX+or+WeycvL3dLHnyTt2aUfrCGDzoLly5x9GNP5rcbfdwCaVENRTiV779xdnPy2+Nh2lqRvXumpuOVUy530/YZvM3Ny+oN8Y+li78L2rYhyddYAmiQ/vrt4fKfHdn2UcqGKL9yWYHjt9rzZ9bGmBcyOVv5r3JxW/PDCpev25WxM22+77+4agA68le2QtfwXy23+l+8OvFQA9JNqG/4g/+FqN4sAVdaEAB0eR7cOoNVByJPyfM5YgDaHgtSOzR/1nTEaMOguWL7M6Q/yg1E3+gF6sTCr/ZFIN0C6S9Izr+Yv5ZO1Vmy13XxYbb+11mJ3RfQD9GK1Jpq2a6N0AOq4BMNrt+/NLra/lr99Ll+81S+tdXNZ8cMLV9uR7u5TrwFoz1vZF7LQ8oW/qLZPzw68lK+BGj8rm3SYIAGqra0DaG0fuqz5SIAW3+bjCusfg4PWllDjlHu175YuUXfB8mWuDVfsOQVV+dc+mDmSukvSM6+jhm8vQGtLXS1Tey12V0QvQIs5d23XRukA1HEJavHXvO2FitMqh8nef98vNtk3urms+OGFO1xzLdo6gPa8lX0hc62OKWSb/LfzTeCel3KA1vhZj3JEgOpr2l14OUDT1tQJdrb5+14AbW1A1KF51JpfY+ujAdCeBVstc/sHXYCereZdnUTOj+M2l2TDvKrjfc0Ve9Q+ota3FrsrohegPUeAC0qsi9IGqOsSrFngnhWw/LV8Sc78y6ViU7v/zZGt+DULl11QeuEf7iz6NAzQnreyN2RtBSQvfFl+nYdqvZTN/McHNX7W57lm94maStt2DLQBh9q+Y4hxoFWz62p8kIcB2rNgq5caP+gO06v8a7+4WsbukqyZ18l3/3xzPxkEaHN7rm8t9qz0PoC29lFrtuuidADqugRrFrjvzT4sNv3PLn+a+q5W0no3hxW/ZuHKXz7/98N/W7qT97yVvSELHdSOvOabHj0vlWcLC7PGPB0+HVRgbcdZ+JgAbdS4b+jV0UaAVn7tLVrR5/jM3YF5fZuNQ8zUC9AO98IAtGW7DQBd/ak4SsGSsTTfgx8J0PULV/vt0+1L89cCtP1WbgJo+fv5ZmXPSyVAixk1bAhQfemOAz08/3a6S7K9AO0bBxoboI9qN9dTBGjHdhsAujoce5haHqcHQfODopMCNB1IX/6q81l4KUAbR09Wc+t5KQNoNuAz/wkBGlmqVyIVg4RcAVr8MDhA+48s5FO3f9oD0Ppe2rhd+KZX37yerU7Enrvw1uf/MnAMdD1AD4f3UdcAtGvrD9A1S7BmgXvf7HR4/Ooay4ylR+U6WuPmsOLXA3Spx/+U3Ra8fVTffxe+bXLUpWXPS/kprWrnjrvwkaV6LXxxXr72Rh+0ADrFSaTu6dHheaT+rU3Qo7EnkQap0/ntvnkd5mdiF24nkbKQAydVJADt2ooA6roEA8EHAZoS83g//zP8wlcH1WVIw24OK34jQLOF7A4E7QPogc9JpEZt81/sean4q3pUvyLgWu13CFBlTX03pvpf+vTvZvHJKK4WvtECaH2/pWc8SwiAHjZx05rfUWev/g/l0Jf+YUytHwgA2l2SnnnVAhwN7MLXTjPnH6ietSgEaNdWBFDXJVhULzgMY1qdP/qmZMlfFPxa6+ay4gcXbv3fxzpAW5XueSv7QtZ9innnZ4x6XioLW+7EcxhTXCnfD7SEZM649JDO5APpWx+M2oZx30irlPq1a56z41/9Y9Of9Q0ZlwC0uyTdedU+RcdDZ+Grj1N9QH5rLcoA2mMrA6jjEhRyG0ifvfjCn61+vhpNf3ZoctmKH164GvW6m3jPagRvVbqnFusG0tfG4JZbFf0vFe/Y6otqIP0zDqSPoGnvSJ+NoVvdzad+R/r05dP/qzg4nxWj6mbatrCXcrY/GBkiv8gvUuzsrGV1T05nA/8e/3N2JmXvz3sXrPhgNn4gAWjPknTndZDvS2dXHQ4AtLyUs9zJ7K5F+RZoy3Z9lPIN7F0rg0tQX08bL+VMdVRWZnWU9trQ5PIV379wq3WbIjSdd4t6JUB7Kt1Ti56Q9Vkle8Wln+XfovZL5S5TuROfzvPluwMXElNTa+JnIhX3QWqew6ydBX1rvwJoUuwYN06PLvoBetDe0y60GaD1k7DtU+757zWUb492Fqx+3HalvSsygHaXpDuv48Yq7Aza6SzympuJSADatXWKcq1+OtBhCUqtfdtrypar+lNbvH/r3JxW/PDCNerQpV77fWtXul6LnpCtZE2b7kslQMuD9fV5EqDqmvqpnPXBMKfLT/2z4g5c147ztuVPELtY+2F1y6/AAK0967D/QaGPLtcqWY39ay9Y+cEsPip710Rn4XuXpDuv8gO89xc36sdeGyr/UpUH2dprUQjQru2GKMUbWO3LuyxBpXVve03p+sm9y+M/G9ycVvyahStvFVg7HlW4lqOIeirdU4tuyJrKz8pqj6f3pcMGXM82fuelvyNA1TX9c+GLR6w3bke7ugfscm+mbNvim3TU9o+zL7MnyZ+qbjobGqDpAYV0mc4PPrLs5J8up4uzd768l3LPgtU+mN+myD3/pWwY08CSdOa1uvXv+Q/u9o2QKZe4c0Pl1lqUArRjuylK/gbWfuawBHUNv+11VUcbW0POBt2cVvy6hXv8aXanu/MfdApXG4bZU+m+WrRDttZAUruBdd9Lh43FXMXPnNMkB+sG6FGTaHqAUtTOqjGOPbbWj3CmJhEBSlHeau3uRFBty7l3z4SaVgQoRXkrPkBrl/sdDh3VoqYTAUpRvjr5u+jMysZPpacXsnGCPIekLQKUoryUjzGKzayDpBI3QNVFgFKUl1bjtobvVq+kakBW39AoamIRoBTlpXTo7d42MCt/bP1b/UOjqElFgFIURXmKAKUoivIUAUpRFOUpApSiKMpTBChFUZSnCFCKoihPEaAURVGeIkApiqI8RYBSFEV5igClKIryFAFKURTlKQKUoijKUwQoRVGUpwhQiqIoT00H0D+lKIraNakBNNyskpjbyffjOcezRo0d0xoytrmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSZKdjT1bao2ztY4ToFJhkgQ0NgEaWrPZWoKa6zgBKhUmSUBjE6CBNZutJ6i5jhOgUmGSBDQ2ARpWs9kGgprrOAEqFSZJQGMToGFFgPqLAB3tbK1cgZx3kSQO1rsYmwD1FwE62tlauQI57yJJHKx3MTYB6i8CdLSztXIFct5FkjhY72RsnkTyFgE62tlauQI57yRJNlvvZmwOY/IVATra2Vq5AjnvJkk2Wu9obA6k9xQBOtrZWrkCOe8oSTZZQ8Y213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11PAhAn//u+nz+H/42/+bj5Tfvft118plzvwjQCObxnCFJAhrbXMdDAPTpe/NMf5l+88Pqm5/8vuPkMecBEaARzOM5Q5IENLa5jgcA6POP5q9+vXj+/+av/Hr53a35a18vnn40f+37tpPH0g2IAI1gHs8ZkiSgsc11PABAH+abm/fmry8WT65n3/zwXkbThpPH0g2IAI1gHs8ZkiSgsc11fDxAlxugv6i+yyia/f/ztpN4zoMiQCOYx3OGJAlobHMdHw/QH96rH++8ldP0YQ7SmpN4zoMiQCOYx3OGJAlobHMdHw/QJ9df+/5f/+N8/uo/LtLN0XzXPX21cMh1P5iSJNy8KIqifBUEoB+vzsL/nAClKApJ4wH6MB3A9P3i+e/Ss/A1gLYHMnEXfrSztd2bQM6Q+7Kgsc11PAhAV+eLbs1f79sCLZ08lm5ABGgE83jOkCQBjW2u4yF24WvMJECndLZWrkDOkCQBjW2u4yEAmu+sZ1/wLPyEztbKFcgZkiSgsc11PMQ40Hyj82F69VEx/pPjQCdwtlauQM6QJAGNba7jAa5EupVvbN5KmckrkSZ0tlauQM6QJAGNba7jAQD65Hp686XVWfj8wnheCz+Js7VyBXKGJAlobHMdD3E3pofXs2Ggr2RHP5/ybkyTOVsrVyBnSJKAxjbX8SD3A32a3gL0P39dfLPk57vt7U8CNICztXIFcoYkCWhscx3nHemlYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0nYObD1bytk7rLVEBKi+ucc0BKhULJe2c1jr2UxAUAJU29laxwlQqVgubeeg1rOZhKAEqLaztY4ToFKxXNrOIa1nMxFBCVBtZ2sdJ0ClYrm0nQnQzZIc2HXwDjcrqbO1jhOgUrFc2s4E6EaJDuw6eAebk9jZWscJUKlYLm1nAnSTZAd2HbxDzUjubK3jBKhULJe2M08ibZDwr4KDd6D5eDhb6zgBKhXLpe3MYUwbRIAGMveYhgCViuXSduZA+g0iQAOZe0xDgErFcmk7Q47nIUAjmHtMQ4BKxXJpOxOgm8STSGHMPaYhQKViubSdCdCN4jCmIOYe0xCgUrFc2s4E6GZxIH0Ic49pCFCpWC5tZwJU3Tues7WOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCSZLrbDRUuYbzYBqiKWS9uZAA0pl8vmMd9sAlRFLJe2MwEaUE43bsJ8swlQFbFc2s4EaDi53ToU880mQFXEcmk7E6DhRICuM/eYhgCViuXSdiZAw4kAXWfuMQ0BKhXLpe1MgIYTAbrO3GMaAlQqlkvbmQANKJ5EWmPuMQ0BKhXLpe1MgIYUhzENm3tMQ4BKxXJpOxOgQcWB9IPmHtM4AvSOx6zbTuNnUYgAjWAez5kAVfeO52yt4w4APflkP0mSU+/c9Vqoymnc5HURoBHM4zlDkgQ0trmObwbocYrPVKe/8luswmnU1A0RoBHM4zlDkgQ0trmObwTos0vp5ue/e3H575lR26AE6Ghna+UK5AxJEtDY5jq+EaCHSbL34fL/28sN0Wt+y5U7jZm4KQI0gnk8Z0iSgMY21/FNAD25UXBzSdKzXotVOI2ZuCkCNIJ5PGdIkoDGbqR2GC8Q1txjGgeAZhug2bHQUfvwBOhoZwJU3TuiNWTsemqXEathzT2m2QTQZ5eSF75qfeUnAnS0MwGq7h3RGjJ2LbXTNVNhzT2mIUCl2opyRTCP5wxJEtDYVWq3q/bDmntMQ4BKtQ3limEezxmSJKCxCdBhp/vBlCTh5kVR1BaqAmjsJVkrboFKtQ1/nWOYx3OG3BQDjc0t0GGnEdO2RIBGMI/nDEkS0Ng8iTTsNGLalgjQCObxnCFJAhp7F4cxdeUFUgJ0tDMBqu4d0Roy9s4NpCdA29qScqmbx3OGJAlobHMd33gl0s2rXb3pc0USATra2Vq5AjlDkgQ0trmO8470UrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY67grQk28/++yz73g3JpZL3xmSJKCxzXXcCaCP3i8e67H3tvfj5QjQ0c7WyhXIGZIkoLHNddzpoXKNMUw/9twMJUBHO1srVyBnSJKAxjbX8c0ATe9JnySn0uFLV7INUc/bKhOgo52tlSuQMyRJQGOb6/hmgB4smflSseP+6P3E98keBOhoZ2vlCuQMSRLQ2OY6vhGg6VONL9a+Lx4yJ3fymahfBGgE83jOkCQBjW2u4xsBetDe4jxoAtXdyWeifhGgEczjOUOSBDS2uY67P5Wz0JHnUVACdLSztXIFcoYkCWhscx13uJlIa4/d9+mcBOhoZ2vlCuQMSRLQ2OY67n4/0OFXHJ08phkQARrBPJ4zJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6zmciScVyaTtDkgQ0trmOE6BSsVzazpAkAY1truN8qJxULJe2MyRJQGOb6zgf6SEVy6XtDEkS0NjmOk6ASmW/XLOl5OZhvD2ESRLQ2AgAffyd12M9CNDRzmGsZzMfgpqP7ecd0RoyNgBAOYwpmnMQ69nMi6DWY3t6R7SGjE2ADjt5TDMgAtRfs5kfQY3H9vWOaA0ZmwAddvKYZkAEqL8IUJF3RGvI2ATosJPHNAMiQP1FgIq8I1pDxiZAh508phkQAeovAlTkHdEaMjYBOuzkMc2ACNAR4kkkiXdEa8jYBOiwk8c0AyJAx4jDmATeEa0hYxOgw04e0wyIAB0lDqR3945oDRmbAB128phmQARoBPN4zpAkAY1truMEqFQsl7YzJElAY5vrOG9nJxXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY7zfqBSsVzazpAkAY1truO8nZ1ULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jouAuiJ1wPhC6cR07ZEgEYwj+cMSRLQ2OY67g7Q25fTu4g8++nbPhfCLwjQAM7WyhXIGZIkoLHNddwVoCefrG7D9OxSctrnXkwEaABna+UK5AxJEtDY5jruCtCDJDn9s/0Xvjr5ZZKc8doGJUBHO1srVyBnSJKAxjbXcUeAHiXJO4tnl9IbgX6zn1zzMCJAxztbK1cgZ0iSgMY213FHgB4kFxc5QBeHyVkPIwJ0vLO1cgVyhiQJaE/K3XEAACAASURBVGxzHXcD6MmNvQ9LgB7v8470cZytlSuQMyRJQGOb67gbQFfozAGa/yd28phmQARoBPN4zpAkAY1truMEqFQsl7YzJElAY5vruOsufHriKCfnkd9peAJ0tLO1cgVyhiQJaGxzHXc8iZSdOFoBdAlTnkSK42ytXIGcIUkCGttcxx0BeryfvHw3A+ijN5L0hJKHk89E/SJAI5jHc4YkCWhscx13HUh/mCTJuf29Cy8u/7/o4UOABnC2Vq5AzpAkAY1truPO18L/YT/J5cdPAnS8s7VyBXKGJAlobHMdd7+ZyONPzy3peeqlLz1cMifP6XpEgEYwj+cMSRLQ2OY6zvuBSsVyaTtDkgQ0trmOGwDobKnmKwRoBPN4zpAkAY1truMSgH5XyOu+yr4Anc06BCVAI5jHc4YkCWhscx13vh/o+0klzSuRZrMuQQnQCObxnCFJAhrbXMcdAfrsUhIHoLNZD0EJ0Ajm8ZwhSQIa21zHna9ESk699Vmhz/Uu5SRAa87WyhXIGZIkoLHNddz5WnivyzcbTl5TEaA1Z2vlCuQMSRLQ2OY67no3Jr/LNxtOXlMRoDVna+UK5AxJEtDY5jouuZ3dOPEk0mhna+UK5AxJEtDY5jouuSP9OHEY02hna+UK5AxJEtDY5jrufBLJ8wr4mpPvhBxIXzhbK1cgZyfrbkuCeIefpbM1Aapu7jGN8zCmvXc85t5wGjl9TQRoBPN4zi7WPfspQbyDz9HdmgBVN/eYZhNAT25ezXQ5SfYuXM31Ju9IH8XZWrkCOTtY9x0pD+IdeoYCawJU3dxjmk0AbY6gj3AlUp8I0Ajm8Zw3W/eO1QjiHXh+EmsCVN3cYxoCVCqWS9uZAFX3judsreMG7sbUFQEawTyeMwGq7h3P2VrHCVCpWC5tZwJU3Tues7WOO44DvVk7b3R85Uc8iRTF2Vq5AjnzJJK6dzxnax33uBLJ87IkAnS0s7VyBXLmMCZ173jO1jruAdDjfQI0jrO1cg1IiDoOpNf3judsreMbAdp3Gv4Md+GjOFsrV7+kG4uYJAGNba7jm7dAj7oAveazdAToaGdr5eqV+HAlJklAY5vr+GaAnvzm6tUr+9VlSFff+sJr6QjQ0c7WytUn+QlzTJKAxjbX8a2/nV2fCNAI5oHmQ4C6WkPGNtdxj2FMniJARztbK1efCFBXa8jY5jrOgfRSsVyjRIC6WkPGNtdxAlQqlmuceBLJ0RoytrmOE6BSsVwjxWFMbtaQsc11nACViuUaq0kG0k8jAlTb2VrHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACVCqlctZ1tpNg174jWkLEJ0GGncLMiQHVUP90DFLvuHdEaMjYBOuwUblYEqIoaA45wYje8I1pDxt5hgD7+9FyS7J17+46HS+bkOV2PCFANNYe8w8Rueke0hoy9uwA9LO/FdNHDZuEN0L4xLwSohghQAlTfeVcBmvLz1IWrV170JqgfQHtHXROgGiJACVB95x0F6PF+cubL7KtHN5K9D3t+48n1177Pvnj+8fX5/N2vu04eS9e67q/4igDVEAFKgOo77yhAD6q70J/cSM52f+H5R/MVQH94b57qJ7/vOHksXfMzXH5FgKqIJ5EIUHXn3QToyY3aVufxfs8jPe7Nc4Demr/29eJpgdO6k8fSNQBafUmA6ojDmAhQbefdBOjGp3I+uZ4D9Mn1bNvzh/de+XXbyWPp6gCtfU2AKokD6SNaQ8YGBehyB/5vVsdA781fz165N/9528lj6QjQhrO1cgVyhiQJaGxzHXfdha89SO6o+1TOW/PX85NIt+a/yF55mIO05uSxdPXDcASouXIFcoYkCWhscx0PchLp4XL3fQXQ5x/lu+7lSfmlQ677Plohs/oq+y5JvOZFURQVVO7DmE6vHsb57RudYUzZAc+JAHq/4GdF0PsEKEVRWyHJQPrk3LlzfZci3UqPd3YA2h7INP5STg5jimaNGpu78NrO1jrufCnnN/v5lZx777R+ci87/z64BVo6eSxdS3gD6WunwQ2WK5AzJElAY5vruPvNRE5uX1lugV74oH0C6cn1jJkaAC0XEgWg9YGYBssVyBmSJKCxzXV8/O3s7s1LLffaA5+F7xcKQBuXAhksVyBnSJKAxjbXccdhTDffrDY8j6/8qL4V2gRoMf4zzDjQAYEAtHkhq8FyBXKGJAlobHMdD3QlUrnPHvZKpAERoPrC/EwRoNrO1jruAdDj/TUAff7R/NVw18IPiADVF+ZnigDVdrbW8Y0AfXYp6ajnZiLlWaOnIe/GNCACVF+YnykCVNvZWsc3b4EedQF6rfNLtdPuTz9e8vPd9vbnzgO0MeAokHgSKXOGJAlobHMd3wzQk99cvXplf+/C1UJvfeG1dLsN0OaAo1DiMKYFKklAY5vruMcxUE/tNEBb24rBxIH0qCQBjW2u4x7DmDy1ywBtH62cyNlauQI5Q5IENLa5jvO58FIRoNrOkCQBjW2u4wSoVASotjMkSUBjm+s4ASoVAartDEkS0NjmOk6ASqV6EqnpbK1cgZwhSQIa21zHCVCpNIcxtZytlSuQMyRJQGOb6zgBKpXiQPq2s7VyBXKGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcdencr70pcfMm05jZ1CJAI1gHs8ZkiSgsc113PVSzt4biMicxs6g0GxGgEYwj+cMSRLQ2OY6bu9a+FkK0KnP16wRy6XtDEkS0NjmOu54LfyN9rPgPZzGzmCl2Qqg8QjKcmk7Q5IENLa5jjseAz1Mzow9CBoGoLMCoNEIynJpO0OSBDS2uY47AvTxb5PkVHlLUK9bMxGgo52tlSuQMyRJQGOb67jzSaS6vA6IEqCjna2VK5AzJElAY5vrOAEqFcul7QxJEtDY5jpubiA9TyJFMo/nDEkS0NjmOm4OoBzGFMk8njMkSVRjP+hKz7whcx0XAfTkjodD6TRi2oY4kD6KeTxnAjScelAp0RSL1JC5jrsD9Pbl9ODns5++7fl0JF7KOdrZWrkCOROgY+QDxvtOU4VZvpaztY67AvTkk9XZo2eXktN+FyURoKOdrZUrkDMBKtZI6g1ZT09Scx13BehBkpz+2f4LX538MknOeG2DEqCjna2VK5AzAeqqUHhzsp6Eo+Y67gjQoyR5J78i/pt9vxuLEKCjna2VK5AzAbpJoTcKZbGDOlvruOvt7JKL5S1FDpOzHkYE6Hhna+UK5EyADmqinWmv2EGWxFzHJTcTyQF6vB9xIH0mAjSCeTxnArSr6Y5CZt7+k449eGCt45Lb2eUA9by3XTiAPiBAI5jHcyZA65qUnIX36Dn4LqO5jhOgUmGSBDT2NgFUg5yFd5C5+CyvuY677sKnJ45ych75nYYnQEc7WytXIGd0gE67u97rHXJmoiX3fbODPBR3wpNI2YmjFUCXMI18EokAjWEezxkZoMrkLLyDz9E1hOebnd1jaDRBJwTo8X7y8t0MoI/eSPzuTk+AjnYmQNW9oznHIWeuaWK7BPJ7s2ezIASdciD9YZIk5/b3Lry4/P+ihw8BGsCZAFX3juIaFZ6ppou9KZbXmz2bhSHopNfC/2G/uBuoHz8J0PHOBKi6t7ZhRc4djr2GorsL0MXjT88t6XnK+wHxBOhoZwJU3VvPqr3ZudOxB7ewdxigY0WAjnYmQNW9VVx6d9lhYjeddxOgJzdrz5E7vvIjDmOK4kyAqntP7hB2WyyQ9Kzb4Xf0JFJj7DwH0sdyJkDVvaec+fozRTsbu6XmOtjRYUwNZka/Fp4AjWEez3kXSbL5NPtOxh7S6CO/2zuQvvVAzky8EimOMwGq7j3FTN2GKO1c7PXagrEHE22BHnUBqnk/0J6aEaAxzOM57xBJBOM7dym2k6KOfM00DUBPfnP16pX9vQtXC731hdfSEaCjnQlQde+A8xIOjt+V2CLFRajSMVBPEaCjnQlQde8wsxGyc2VtP7aP8/2I26FKw5g8RYCOdiZA1b3Hz8IHnpm17di+zvcj7stPPpA+xnPhCdDKmQBV9x41tS87V9ZmY49yzq2jIHRagEZ6LjwBWjkToOre3lOOgmdmbTL2aOfKWh+hUwI02nPh+wFq7FRdIGcCVN3bZ6LR7FxZW4sdxrlurY3QKQEa7bnwAwC1daoukDMBqu4tnSAMPDNrS7HDOTetdRE6IUDjPRd+EKD+K3bcVQtbUy5l83jOVkgSjJ0rayuxwzq3rTXPKE0I0HjPhe+uugfZMVD/tTryutktKpeqeTxnAyQJt+FZWRuIPYFz11oPoRMOY4r3XPghgHojdOydW7aqXIrm8Zy3niSTfMS3P/YkzgPWKgideiB9nMcad9bbg/IsvE9tR987cPvKpWMez3mbSTLd5tFWx57OedBaYTMUDqA+f5gIUE/zeM7bSpIJ9tvr1tsae1rnNdaTI3TKXfh4z4VfC9DsW9E6JUA9zeM5byNJJmXnynobY0/vvN564lXuMc32Pxd+E0AXsnVKgHqax3PeMpJMu+FZWW9ZbCXnTdZTrvgJARrxufAbASpcpzyJ5Gcez3mrSKJ2Tni7Yqs5b7aebvVPOZA+3nPhNwM0X6euUOQwJi/zeM5bQxLFIYnbFFvV2cV6svN2HtNs/3PhXQBarFN3gnouzGLryzWZeTznbSCJ0n573XobYus7SwaOBTf3mGb7nwvvBtDZbJtH2wZyJkDVvdN/9OGZWceOHcfZ2XqC92NagI7VpABd7ZbrHJua3GHQmQBV9454c0oCdIOCvyvwAM0Q6mnjLBvlCm8ezzmadTR4piJANyr0ezMtQP/4WanPt2kcaKZZjaAT991IuYKbx3OOYh1lt70uAnSzAr9DUwK0OoeU3RbUx8ljmlQygE5/KNRKuUKbx3PWtq4d84wJMQLUQUE/7hMCtPlsY32A1k6cPxg4iVSOTpr6CpHRc/AdBUCAaqix5UmAajvLrcN93Ce9lHPvg+9KeT0aaQxA60M3l9/3PdKjMbpz2y5XaMh7HCoBOrG6e+3O1uOGxvWJAHVWqI/7lDcT8buJcsPJc7rlqmlcPDQA0FaFt+tyhbr8r4QiQCdU/1glV+uRF2f0iQAVKMynfeq7MY2TL0AX2TVGVUOHANqdbBqEjizXiGvxCdCJNHzCyNF67OXBfSJARQrxYZ9yF94eQLfpcoWaCFCx87TWa8+2u1mPvkFNrzUBKlKAD/ukd2OKtgvvD9BpEEqAajtPZ71xqBIBqu3sbz3+wz4hQJ9d8rsFU93Jd8IRAF1MsCdPgGo7T2Ldf8yz4+00LwI0nPMo65Ef9inHgT66lJy+WuhNzYH0NYDm34oAGhyhPImk7TyBtesoeQJU23mk9ajP+pQA/STaONDqNHz+nRSggRHKYUzazoGtHdm58nabJU8iBXMebT3ioz7pMdCoAK1BJ/1OCtCgB0M5kF7bOZy123573dvx98LzkwD1lf8nfeKB9D777XUn3wnLS5Hy64x8ABoQoabLNcI8nnMYazE8M2/XXwzOTwLUW94f9SnHgY4+hzR+CzQ9FuoP0EWoPXnb5fI3j+ccwlrOzpV3AOu2HFlLgPrL85O+swPpc34+SG85PwKgYRBqvVy+5vGcA5xX8H3fJ0jturdPgI6Szxu+owPpV6fhU3oWHwRvgMYaLBZGBKhYXvvtdW9/6wE5n28iQMfJ402f9CSS55OQak7eU+YAzTdDF6MAOh6hu1AuH/N4zv6XR4/+cxk8tfuIJwJ0pOTv/YQAPbmx947H3BtO3lOm7HyQjwbNXhgF0MXIPfmdKJeHeTxn7xv0bOHxGgJ0g3NIa2kBJtyFv3klSfYuxBlInx0ELfmZ/jsWoKMQuiPlEpvHc5Zah2HnyjvETOoiQDc4h7WW9WDKk0gRx4GmAM03QPMCjgfoiE2UnSmX0Dyes8A6yH573TvMbCoRoBucp7hqwtncw8ACQLO1UONnEIB6I3R3yiUzj+fsaB0anpl3uFnl4kmk9c7BrQWFmPJSzvEaAdByTMKsAGiwQcs6R5rDCA+gGWncYgdnZyYOY9J2nubGB47mHjO3AdBC4QEqR+hulcvdPILn6t3eGDv8hmcpDqTXdp7C2rUcBKifZB+/HSuXs7m+ZbG3u856iv32mmJCjAANJseCTAvQx5+eS5K9c297PVFusc0AlW3D7Fq5XM3VHTedb5mYnZkIUG3niaydajIpQKv7MXkOqQ8B0KAnkZpy/izuXrnczNUd1wFUA56pCFBt58msHcoyJUBTfp66cPXKi94EDQLQcMOY+uT0idzFcrmYqzsOAFSLnZkIUG3n6aw3V2ZCgB7vJ2e+zL56dCPxuzNTGIAGGkg/JIdP5k6Wy8Fc3bEXoIrwTEWAajtPaT3FR9sRoAfJmeLqo5MbyVkPo1AATTUdQIM9Z2wKgQG0cxJJl52ZCFBt50mtN9Rnyrsx1bY6j/fP6F7K2dGUAF1s+qDuark2mUfwrIYxqe6310SAajtPa72+QVNeiVS7+Mjz5qB2AJpq+NO6s+XaYB7DNNt/jwXPVASotvPU1qF3LgnQIQ19ane4XGvNo7hGZGcmAlTbeXLrNXWachc+uVZ+c5Ts+C58qd4P7y6Xa525tmG14QlJEgJ0Kg0TlCeRJlBnde90udaYa5o19toxSQIaWyd1wKNz7sOYTn+RffXtG3GHMaXSBGhnX37XyzVkruTTPeKJSRLQ2Eqp+zdCpx5In5w7d87/UiSzAG0hdOfLNWCu4NF/ugiTJKCxtVL3EnTSSzm/2c+v5PR9todhgGYqPtq7X65+82lnv+ZUOyZJQGPrpQ50ftj9ZiInt68st0AvfOBzAilz8pyuR1EAqn4VTEe7CtAN45QwSQIaWzF1t2+7fzu7XJEAmiomRHcOoA8ebGDnyhmSJKCxNVMHGWBDgEp13+VDP43zLgHUDZ6ZMyRJQGOrpm6Xb1qA/vGzUp+DjAPtVbaWoyB0VwAq/AuESRLQ2LqpWyWcEqB/2B/9ULn7wZQk4eY1QjkFYi+GKVXwjL0kFHU/K+So6R0BehT1qZxtxd8CzaW7N296C9R9l73jDLkpBhpbP3Wtj5Neyrn3wXelvJ7qsYsATaUHUbMA9Ydn5gxJEtDYEVI/GHWNjPNz4a/1/kDiNHYGlbYKoJlUKGoRoAFWDCZJQGPHSF2WU+luTJ7aaYCmmhyitgA6brOz7gxJEtDYUVI/GHGRoesuPAFaaP1anhKiRgD64EEwdq6cIUkCGjtO6rypE56FP+QufKHNa3mqTVEDAA2LztwZkiSgsSOlXtV1QoA+u+R3C6a608jpa9pygKaaAqLbDNApyFk4Q5IENPY2d7xXruNAH11KTl8t9Cb8QHo3BabKVparucO+a0cuCFBt523s+Dq5AvQTjgPNJVzLAemyZeWanJyFMyRJQGNvWcc3y/kYKAGay+8dDsGabSmXFjkLZ0iSgMbelo47SzCQ3vc+doXTuMnrMgjQVGP3diOX60FbWs6QJAGNvaMADXAOiQAt5E2haOWKRM5cmCQBjb2zAOU40EKh3mE5lbTLFZebpTBJAhp7RwHKgfSVwr7Dnf3iNZxSKVfPAnmPkQsjTJKAxt5RgC4OPZ8kV3caO4NKOwTQUkPgqjtPVa5e75Y95meKANV23lGAntzwfZZc5TRy+pp2EaClhmj2IGC5Bj2GN4AxP1MEqLbzbgL05OaVJNm7wIH0C9VyrePcOvLJphuGZl2YnykCVNt5NwH67BLHgRballPhoyS3x/xMEaDazgTooJPHNAOCBOimco3j42bzCebp6AxJEtDYOwrQECJARztbK1cgZ0iSgMY213ECVCqWS9sZkiSgsc113BGgBy996THzptPYGVQiQCOYx3OGJAlobHMd5zORpGK5tJ0hSQIa21zHeSmnVCyXtjMkSUBjm+u466WcvJlIIZZL2xmSJKCxzXXc+VLOM2MPghKgo52tlSuQMyRJQGOb67gjQB//NklO8UqkVCyXtjMkSUBjm+s4B9JLxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4xxILxXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY6LAHpyx8OhdBoxbUsEaATzeM6QJAGNba7j7gC9fTk9+Pnsp297Pp2TAB3tbK1cgZwhSQIa21zHXQF68snq7NGzS8lpv4uSCNDRztbKFcgZkiSgsc113BWgB0ly+mf7L3x18sskOeO1DUqAjna2Vq5AzpAkAY1truOOAD1KknfyK+K/2fe7sQgBOtrZWrkCOUOSBDS2uY673s4ufSpnfkuRw+Ssh5FNgM6War3Ecmk7Q5IENLa5jktuJpID9HgfZiD9bNYlKMul7QxJEtDY5jouuZ1dDlDPe9sZBOhs1kNQlkvbGZIkoLHNdZwAHdZs1kdQlkvbGZIkoLHNddx1Fz49cZST88jvNDwBOlbmyhXIGZIkoLHNddz5fqBnC4AuYQpyEokArZvHc4YkCWhscx13BOjxfvLy3Qygj95I/O5OT4COlblyBXKGJAlobHMddx1If5gkybn9vQsvLv+/6OFjEaA8iVQ3j+cMSRLQ2OY67nwt/B/2i7uB+vHTIkA5jKlmHs8ZkiTbELtnFPTkztY67n4zkcefnlvS85T3A+ItApQD6SvzeM7xSRLFOnrsvs2HyZ2tdZz3A5UKkySgsZEB2nsAa3Jnax0nQKXCJAlobGCA9p9CndzZWsclAP2ukNd9lQnQ0c7WyhXImQBV914QoI5yvh/o+3yo3EqYJAGNTYASoOvlCNDmYzkJ0DjO1soVyJkAVfdeEKCOcr4SKTn11meFPse4lLNfmCQBjQ0MUJ5EcpLztfBel282nMbOoBIBGsE8nnNskkSyjh6bw5gc5Ho3Jr/LNxtOY2dQiQCNYB7POTpJ4ljHj82B9JsluZ3dOBGgo52tlSuQc3ySRLGGjG2u45I70o8TATra2Vq5AjlDkgQ0trmOO59E8rwCvuY0dgaVCNAI5vGcIUkCGttcx52HMe294zH3htPI6WsiQCOYx3OGJAlobHMd3wTQk5tXM11Okr0LV3O9yWFMUZytlSuQMyRJQGOb6/gmgDZH0HMgPcul7wxJEtDY5jpOgErFcmk7Q5IENLa5jvNuTFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+u44zjQm7XzRsdXfsSTSFGcrZUrkDMkSUBjm+u4x5VInpclEaCjna2VK5AzJElAY5vruAdAj/cJ0DjO1soVyBmSJKCxzXV8I0D7TsOf4S58FGdr5QrkDEkS0NjmOr55C/SoC9BrPktHgI52tlauQM6QJAGNba7jmwF68purV6/sV5chXX3rC6+lI0BHO1srVyBnSJKAxjbXcd7OTiqWS9sZkiSgsc113GMYk6cI0NHO1soVyBmSJKCxzXWcA+mlYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5ejXdExwxSQIae6s73icCVCqWq08TPkMckySgsbe5470iQKViuXo0m01HUEySgMbe4o73iwCViuXqajabkKCYJAGNvb0dHxABKhXL1RUBOoE1ZOzt7fiAHAH6+Lu6PHwI0ADOW1suAnQCa8jY29vxAbleC9+6H9Opt8WXdgYB6OozSoBGMF/3QwJ0AmvI2Nvb8QF5AjRJ9j6UOnksXVv5h5QAjWC+9qc8iRTeGjL2Fne8X443E/njb5fIfPOzzz67uZ/svf3Zry7Ln20cAKDFx5QAjWC+/sccxhTcGjL2Nne8V47HQJeboBfzL7/J0PmoesHVSbxsbc0I0HjiQHpta8jYW93xPjkC9KCGy8Pk7CK9Ub3wwR4E6Ghna+UK5AxJEtDY5jruuAt/o3bI83g/Raf40XIE6Ghna+UK5AxJEtDY5jru/Vhj8U3qCdDRztbKFcgZkiSgsc113BWgjS3QSADlSaRo1qixCVBtZ2sddz4Gerb1dYRjoBzGFM0aNPZ0Z8Y2iwDVN/eYxhGgR0ny8oqXJ59kjzX+Zl//LPyCA+njCTH2hGOzNosA1Tf3mMb1WviDJEnOX7169dzy/7PZwHrpSHpeyjna2Vq5AjnHsp7y6oDNIkD1zT2mcQVouuGZ68d3U4DuXZM6eSzdgAjQCObxnCNZT3p96mYRoPrmHtO4343p8afnsovg7yzSxxz/fZxr4VciQCOYx3MmQNW94zlb6zhvZycVy6XtTICqe8dzttZxAlQqlkvbmQBV947nbK3jEoCW9wO942FEgI53tlauQM48iaTuHc/ZWsddAfro/dqt7KQ3Ylo5eUwzIAI0gnk8Zw5jUveO52yt4+53YyJAV2K5tJ05kF7dO56ztY47AvQwSU6/9Vmhz8Wn4BcEaABna+UK5AxJEtDY5jruejem2qWcniJARztbK1cgZ0iSgMY213GPm4l4igAd7WytXIGcIUkCGttcxz1uZ+cpAnS0s7VyBXKGJAlobHMd97ihsqcI0NHO1soVyBmSJKCxzXXc+STSunsv/dtfz+evvPv16pvnH1+fz4tv6k4eSzcgAjSCeTxnSJKAxjbXcUeALjdB3xmcx+/mmV75dfrND+9l3/zk9x0nj6UbEAEawTyeMyRJQGOb67jjLvzNK0myd+Fqrjcbw5gezl/528Xi6UcraN6av/Z1+s1r37edPJZuQARoBPN4zpAkAY1truOuJ5GSwYH0zz+a/yL9f7npufz/yfUMoz+8t9oerTt5LN2ACNAI5vGcIUkCGttcx8cD9If38t31W/OfLxb35q9n39xLv2k6eSzdgAjQCObxnCFJAhrbXMcD3o0pA+it1ebocr/+9baT/5zbIkAjmMdzhiQJaGxzHQ8H0Gyv/flH+a77k+vFQdA/LXQ/mJIk3LwoiqJ8FQ6g2c47AUpRFI42AfTkZnrOfflvXW/23EzkYTaMvqaW1AAAGzFJREFUqQbQ9kAm7sKPdra2exPIGXJfFjS2uY5vAuizS+kpo3UnkVZ6eP2V9OBnzxZo6eSxdAMiQCOYx3OGJAlobHMdDwTQe/kwegJ0Smdr5QrkDEkS0NjmOh7mGOjv5sWwT56Fn9DZWrkCOUOSBDS2uY6HAOjzW/NXiwOexfhPjgOdwNlauQI5Q5IENLa5jocA6K3adZu8EmlCZ2vlCuQMSRLQ2OY67nAWvqvmWfh79even380f5XXwk/kbK1cgZwhSQIa21zHHU4iddW6lHNeKD3s+ZR3Y5rM2Vq5AjlDkgQ0trmOjwfow3kDoIunHy+/ere9/UmABnC2Vq5AzpAkAY1truMBr4Xf5BRuVgRoBPN4zpAkAY1truMEqFQsl7YzJElAY5vruAigJ3c8HEqnEdO2RIBGMI/nDEkS0NjmOu4O0NuX04Ofz376ds+F8E5OfpP1iQCNYB7PGZIkoLHNddwVoCefrM4ePbuUnPZ7wjEBOtrZWrkCOUOSBDS2uY67AvQgSU7/bP+Fr05+mSRnvLZBCdDRztbKFcgZkiSgsc113BGgR0nyzuLZpXT80jf7yTUPIwJ0vLO1cgVyhiQJaGxzHXcE6EH6XPgVQBeHyVkPIwJ0vLO1cgVyhiQJaGxzHXd8rPGNvQ9LgB7v99wP1MHJY5oBEaARzOM5Q5IENLa5jrs+lTNlZg7Q/D+xk8c0AyJAI5jHc4YkCWhscx0nQKViubSdIUkCGttcx1134dMTRzk5j/xOwxOgo52tlSuQMyRJQGOb67jjSaTsxNEKoEuY8iRSHGdr5QrkDEkS0NjmOu4I0OP95OW7GUAfvZGkJ5Q8nHwm6hcBGsE8njMkSUBjm+u460D6wyRJzu3vXXhx+f9FDx8CNICztXIFcoYkCWhscx13vhb+D/vF3UD9+EmAjne2Vq5AzpAkAY1truPuNxN5/Om5JT1PvfSlh0vm5DldjwjQCObxnCFJAhrbXMd5P1CpWC5tZ0iSgMY213HeD1QqlkvbGZIkoLHNdZz3A5WK5dJ2hiQJaGxzHef9QKViubSdIUkCGttcx3k/UKlYLm1nSJKAxjbXcd4PVCqWS9sZkiSgsc11nPcDlYrl0naGJEkWe7ZUFO8Ypitnax3n/UClYrm0nWEBOptFIijmm83b2amI5dJ2RgXobBaLoJhvNgGqIpZL2xkUoLNZNIJivtm8H6iKWC5tZwJU3VvdsXS21nHeD1QqlkvbmQBV91Z3LJ2tdZz3A5WK5dJ2JkDVvdUdS2drHef9QKViubSdQQHKk0jq5h7T8H6gUrFc2s6oAOUwJm1zj2l4P1CpWC5tZ1iAciC9srnHNLwfqFQsl7YzLkCjecdzttZxAlQqlkvbGZIkoLHNdXwTQE/++NnnnjcAbTsFmUsmAjSCeTxnSJKAxjbX8U0AfXYp8bruqMcpyFwyEaARzOM5Q5IENLa5jrsC9Nnlc+fHgZQAHe1srVyBnCFJAhrbXMedATp6S5QAHe1srVyBnCFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxx0A2hUfKhfH2Vq5AjlDkgQ0trmOE6BSsVzazpAkAY1truMb78Z082pXb/KpnFGcrZUrkDMkSUBjm+s47wcqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY09ferZUkPmHrMjQKXa5XKtM4/nDEkS0NiTp57NhglKgGpoh8u11jyeMyRJQGNPnXo2W0NQAlRDu1uu9ebxnCFJAhp74tSz2TqCEqAa2tlybTCP5wxJEtDYBOiwU7hZEaARzOM5Q5IENDYBOuwUblYEaATzeM6QJAGNTYAOO4WbFQEawTyeMyRJQGPzJNKwU7hZEaARzOM5Q5IENDaHMQ07hZsVARrBPJ4zJElAY3Mg/bBTuFkRoBHM4zlDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhsCaHUraQI0gnk8Z0iSgMY213E7AK09zIQAjWAezxmSJKCxzXXcDEDrj9MjQCOYx3OGJAlobHMdtwLQxgOdCdAI5vGcIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdtwJQnkQyWK5AzpAkAY1truNmAMphTPbKFcgZkiSgsc113A5AOZDeXLkCOUOSBDS2uY4bAmglAjSCeTxnSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11XBGg94MpScLNi6IoylfcApWKf521nSE3xUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEq1ZaWq7rVykTmU858vTMkSUBjE6DDTuFmRYB2VLvZ30TmE857gzMkSUBjE6DDTuFmRYC2Vb/d9ETm0816kzMkSUBjE6DDTuFmRYC21HjgyUTmk815ozMkSUBjE6DDTuFmRYC2RIBO5R3RGjI2ATrsFG5WBGhLBOhU3hGtIWMToMNO4WZFgLZEgE7lHdEaMjYBOuwUblYEaFs8iTSRd0RryNgE6LBTuFkRoB1xGNM03hGtIWMToMNO4WZFgHbFgfSTeEe0hoxNgA47hZsVARrBPJ4zJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6ToBKxXJpO0OSBDS2uY4ToFKxXNrOkCQBjW2u4wSoVCyXtjMkSUBjm+s4ASoVy6XtDEkS0NjmOk6ASsVyaTtDkgQ0trmOE6BSsVzazpAkAY1truMEqFQsl7YzJElAY5vrOAEqFcul7QxJEtDY5jpOgErFcmk7Q5IENLa5jhOgUrFc2s6QJAGNba7jBKhULJe2MyRJQGOb6zgBKhXLpe0MSRLQ2OY6Hh6gzz++Pp+/+3XXafScSxGgEczjOUOSBDS2uY4HB+gP781T/eT3Haexc65EgEYwj+cMSRLQ2OY6Hhygt+avfb14+tH8te/bTv7znC1V/54AjWAezxmSJKCxzXU8NECfXM+2PX9475Vft5285zmbtQhKgEYwj+cMSRLQ2OY6Hhqg9+av5///vO3kO8vZrE1QAjSCeTxnSJKAxjbX8dAAvTX/Rfb/wxykNSfPOc5mHYISoBHM4zlDkgQ0trmOBwbo84/yXfcn14uDoH9a6L6fKoCWLyWJ57woiqICigClKIry1HQAbQ9k4i78aGdruzeBnCH3ZUFjm+v49FugpZPvPHkSqXC2Vq5AzpAkAY1truMGAMphTIWztXIFcoYkCWhscx3f/rPwCw6kL5ytlSuQMyRJQGOb63j4caA/b/xfcxo555oI0Ajm8ZwhSQIa21zHLVyJ1BEBGsE8njMkSUBjm+t4aIA+/2j+avBr4dsiQCOYx3OGJAlobHMdD34zkae8G9NkztbKFcgZkiSgsc11PPz9QJ9+vOTnu+3tTwI0gLO1cgVyhiQJaGxzHecd6aViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxwlQqVgubWdIkoDGNtdxAlQqlkvbGZIkoLHNdZwAlYrl0naGJAlobHMdJ0ClYrm0nSFJAhrbXMcJUKlYLm1nSJKAxjbXcQJUKpZL2xmSJKCxzXWcAJWK5dJ2hiQJaGxzHSdApWK5tJ0hSQIa21zHCVCpWC5tZ0iSgMY213ECVCqWS9sZkiSgsc11nACViuXSdoYkCWhscx0nQKViubSdIUkCGttcxxUBSlEUtWsiQCmKojylBdCAai80iBgbSZixzacmQLdXjI0kzNjmUxOg2yvGRhJmbPOpCdDtFWMjCTO2+dQE6PaKsZGEGdt8agJ0e8XYSMKMbT41Abq9YmwkYcY2n9oEQCmKorZRBChFUZSnCFCKoihPEaAURVGeIkApiqI8RYBSFEV5igClKIryFAFKURTlqe0H6POPr8/n734dezF09MN7r+dfNWLv9Dr4t7+ez1/pTbrbsf9qGfu/fL/6Bif2Uk+uv7aKvQuptx6gP7w3T/WT38deEBXdmucAbcTe6XXwuyzb/JVfp9/gxL63iv1qN+lOx17q+UfzFUB3IvXWA/TW/LWvF0+Ldb7ben5rXgC0EXuX18HD+St/u0jDZR8dmNhPrmex/2r1fsPETrX807GKthOptx2gT65nH6wf3lttoey00p26HKCN2Lu8DpabI79I/19ugPwCKPaSFz9P/1tlxIm9SPPlAN2N1NsO0Hs5Ue6tCrfLWv5l/st/LePWYu/yOvjhvXyn7VYn6S7HLrSKjxR7+Rfzb1bHQHcj9bYD9NZqA2W5p/f6ht80r3uv/mMZsxEbYh1kAIWLvTqfghT71vz1/CTSbqTecoA+/yjfpC/P3O228v40YkOsg2zfDS72v15PuYEU++Fy932VbEdSE6BbJViAZrtwYLFvzeev/OMCKnb2d5IA1VNtxZob4OCjLkB/8nuEdfAwG8aEFfv5//5P1+ev/Feo2NmBmg5ALae2A1Bjf5n8BLoF+vD6K+khMLTYi8W/pfvwOLHvZeffuQWqJ7sr1k+YAL2XD6MHi50qPSYIE/vJ9SwbAaoos2fn/AR5Fv5382LwH1TsTBkwUGLnV1/lVxztRuptB2gxLszc+DA/PWwNh8vHyO3yOnh+K7+ecQEUu7h+YAVQlNhNgO5G6m0HqNkrFPxUAHQ3rtJw0q3a1Xs4sW+VuxqvI8XOlO+m70bqbQfo8k/1qyavkfVTAdBG7J1eB/fqqXBiP7k+/8vvF89/l48+QImdKQfobqTedoAunhq9S4ufykNAjdg7vA7ym/DM87sAoMRO3+nVTaiyPXmc2KmKE0U7kXrrAbp4+vFyvb5r7O+Sr6pj6I3Yu7sOHlaHxbLkILGXelq/DSpO7EXtTPsupN5+gFIURW2pCFCKoihPEaAURVGeIkApiqI8RYBSFEV5igClKIryFAFKURTlKQKUoijKUwQoRVGUpwhQiqIoTxGgFEVRniJAKYqiPEWAUhRFeYoAhdFB0tTF7LUzd0fN9DB54avafE4+3U+SFz4s/59cakaDzj1LcLx/dt2UTiv95Ea2YqktFwEKIw2AZh7LV4r/J9f0Rt/8qH8NDUd9dmn9Armt9OP9kW8NpSECFEYKAD3eT/6k/v/kmt5oaA2tiXqQrVr5LDu/tn421DaIAAXTyY1k7f6lUIeNja+jJLlW/39yTW80RLvhqKE2HY/3uRO//SJAwTQxQPc+rP8/uaY3GgboQNTlCg7E9IOg7xQ1iQhQMBGgMokBehTskOzxvtJapPxFgIKpCdAVHrLXbr+RJHsvL7+7fTlJkpe+zH/j0fv7y9fLb0ulv7X89fox0MPVsdW9K/n/H7YnTzfOUptTnZ88u5RcPPn0xeWP3r5bNz7/waL+bWs5CsMPh2e8Llpj1rlTuiDFzJfJDqvDxY359kUtV+nZDc6rld6NnBnUIi/nwKOg2y4CFEwDAP2T/BTTC1990mDCYXHK6eX2XFa//v56gDYnX051tThn3fzJkib//o3V96e/ahjnvr3LUQdoZ8Z7f55bDkVrzaVckAGANuY7DNDj/XwPfti5BGgz8jdFxNNflUvGE/HbLgIUTAMAXfLj7uJk+Tk/lbx0Z3Hy22T12T1cbTY9/qRF0IPy9xtn4dv7ta3JU5/lDx590PnJkiZLMt1dPLqRb+4tf37my8zgYs+MSuVGzRmf/mKx+PaN1cmd4WiFmk4tgFa78K35Du7Cl3vww84lQBuRl+RdLkf2W2fLeXEffttFgIJpCKAZNtIP9dn8BykHlh/q/JcPG5/l5esX85fXAbQ9eepzrZhB4yep8bV88XK6ZDNcfrtuOeoAXc24mLCYcjBaoZbTEEDb8x0EaHnMdNi5Amg9cnk0uVq+amGobRUBCqYBgJYf3nLXfbUHXnyW25Plry8/4msA2p688mn/pORTPutycNDR2uWoAbScccGzFeQHo1VzaDgNAbQ93yGAVss37HzQ+BtRRD7sbm4GPuFHTSACFEwDAG18lhc5F+q/Wz8bXXv9YA1AO5OXPp2fLGlSbmKuaFLfShxajjpA2wHyWQ5FK9V0GgRoe75DAK2mH3YuAdqIfJSeofpiUVc1C2pbRYCCafAsfP59/bOe7mVWqkBTffZbmGlSpTN56dP5SZtbTUwOLUcdoOWp72LC1ZdD0VproG7VB9DOfJ0AOuBcnYVvOK1ONJ16+87gwlHbJwIUTIEAWhyb2z6AdjZ6AwG0tQ0cHqCroU7pWfhyIBMBuvUiQMEkBGjvMTj3LdDm5HWANn+yEaADxwJ3aQs01bc3X0wRWlzKRIBuvQhQMEkAOnQMzv0YaHPyPs6t1AVoNRjo7JpjgW2A9h0D3QTQulNtQZrJvI6BygG6WNTHWfEY6PaLAAWTBKDV+ePWZ/mwRp01Z+Hbk9d9mj9p0+SoNt7p2vBydAF6WG6+HSX5Wfj1AG05lRu7rWTt+TqdhZcAtPtHiWfhLYgABZMIoOng7rv59/VbZJTjMjeOA21MXvm0f9IGaDnGpxyP2rscXYD2jANdD9CWUwnoo2Yyr3Ggoi3QchhT9TeC40C3XwQomEQAza6/+WDRe/nOS186XYlUn7zm3fpJZ3/2qHMlUu9ydABav2Lo4sIBoD1O6bfZbea/Wqy4dnKnM981VyJVHBQBdPn93jvL1x/dqG3s8kqkbRcBCiYZQKtr0FsH4/KLu0//t7UAbU1e927+pHtAcOha+OZydAHavRZ+A0BbTsVV/md+W5J8dSFRc76DAF0mudZa0Y7HQI/3i4zVEAceAt12EaBgEgJ08ej99LxwdYugQt9e7tyNqY8qjckb3o2f9JxR6dyNqW85egCa3zXp5Tsty0GAtpxWd0h6p1yQb5Y/PHu3Pd/h29k17sbU6zx0Eunk03PpQNDCIZ0D9+C3XQQoRYVUyPuBhpoTNZkIUIoKqXDbjXwokgERoBQVVKE2HLkBakEEKEWFVaAtR26AWhABSlFhtem58G7ic+FNiAClqMA63h9/AdHJDe7AWxABSlEU5SkClKIoylMEKEVRlKcIUIqiKE8RoBRFUZ4iQCmKojxFgFIURXmKAKUoivIUAUpRFOUpApSiKMpTBChFUZSnCFCKoihPEaAURVGeIkApiqI89f8BXBGXzv5MTncAAAAASUVORK5CYII=" width="672" /></p>
<div class="sourceCode" id="cb116"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb116-1"><a href="#cb116-1" tabindex="-1"></a>p2 <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rdd66<span class="sc">$</span>ftki, df_rdd66<span class="sc">$</span>running_var2,</span>
<span id="cb116-2"><a href="#cb116-2" tabindex="-1"></a>             <span class="at">covs =</span> <span class="fu">cbind</span>(df_rdd66<span class="sc">$</span>age, df_rdd66<span class="sc">$</span>female, df_rdd66<span class="sc">$</span>income, df_rdd66<span class="sc">$</span>education, df_rdd66<span class="sc">$</span>psu_rul),</span>
<span id="cb116-3"><a href="#cb116-3" tabindex="-1"></a>             <span class="at">x.lim =</span> cutoff_range2, </span>
<span id="cb116-4"><a href="#cb116-4" tabindex="-1"></a>             <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb116-5"><a href="#cb116-5" tabindex="-1"></a>             <span class="at">y.lab =</span> <span class="st">&quot;Feeling thermometer to Prime Minisiter Kishida&quot;</span>,</span>
<span id="cb116-6"><a href="#cb116-6" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Cutoff: The Gold medal confiramtion of women&#39;s judo 66kg&quot;</span>,</span>
<span id="cb116-7"><a href="#cb116-7" tabindex="-1"></a>             <span class="at">c =</span> <span class="dv">0</span>) </span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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sxV/MI5T3K4aYAeFUXxAUpRO6vWOPbU2jzCmYoiApSinNU53Emgxp7z4JEJFVcEKEU5Kz1AG7f7HY2d1aLiiQClKFfN/31yZhXjp/LLC8U4QV5DkhYBSlFOKscYpWbWYbYWd0DFRYBSlJNW47bGZ6sX0npA1tDQKCqyCFCKclI+9HZvCswqH1v/5vDQKCqqCFCKoihHEaAURVGOIkApiqIcRYBSFEU5igClKIpyFAFKURTlKAKUoijKUQQoRVGUowhQiqIoRxGgFEVRjiJAKYqiHEWAUhRFOYoApSiKchQBSlEU5ah4AP0biqKoXZMYQMOtKku5n/xdOud01qixU1pDxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HFQF0f6nV3wjQBObpnCFJAhpbXcf1AHR/vyYoAZrAPJ0zJElAY6vruBqA7u+vCUqAJjBP5wxJEtDY6jquBaD7+w2CEqAJzNM5Q5IENLa6jhOgtmK5pJ0hSQIaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcS0A5UUkheUK5AxJEtDY6jquBqAcxqSvXIGcIUkCGltdx/UAlAPp1ZUrkDMkSUBjq+u4IoCuRYAmME/nDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vruCBAvwumLAu3LoqiKFdxD9RW/O0s7Qy5KwYaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HCVBb7Xi59pcaNBfwHhYmSUBjE6DjTuFWRYBG0/7+CEF3O/aod0JryNgE6LhTuFURoLG0vz9G0J2OPe6d0BoyNgE67hRuVQRoJO3vjxJ0l2Nv8E5oDRmbAB13CrcqAjSSCNCud0JryNgE6LhTuFURoJFEgHa9E1pDxiZAx53CrYoAjSQCtOud0BoyNgE67hRuVQRoLPEiUsc7oTVkbAJ03CncqgjQaOIwprZ3QmvI2ATouFO4VRGg8cSB9C3vhNaQsQnQcadwqyJAE5inc4YkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVddwCoPNvSj367++7ODksMyICNIF5OmdIkoDGVtdxU4CevputdY4ATeKsrVyBnCFJAhpbXccNAXp2vcHP7CIBmsRZW7kCOUOSBDS2uo4bAvQoy/au3Jjl/2V7txx8CNAAztrKFcgZkiSgsdV13Ayg87vZxSf5/2/nLF3+1cXJZaFhEaAJzNM5Q5IENLa6jpsB9Oz63vuLnJ3Xlv8/zDHq4OSy0LAI0ATm6ZwhSQIaW13HTQFaXDc6zi7V/7d3clloWARoAvN0zpAkAY2truOWAM2P3s+uOx3DE6DeztrKFcgZkiSgsdV13PQcaHEIfzLLOVrS1NrJYZkREaAJzNM5Q5IENLa6jhtehT8szn6uToWuMGrv5LDMiAjQBObpnCFJAhpbXccNAXoyy176Ir8Mfy2HKQ/h0zhrK1cgZ0iSgMZW13HTO5EOi/uPjrNsb5YVe6P2Ti4LDYsATWCezhmSJKCx1XXc+F74r/ID9/lhcSMSx4GmcdZWrkDOkCQBja2u4xaTifz/S27OH16+/JYTPwlQf2dt5QrkDEkS0NjqOs7p7GzFckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HtwL02Td9PXbZOgLU21lbuQI5Q5IENLa6jm8DaHseO84HynLJO0OSBDS2uo4ToLZiuaSdIUkCGltdx7cBdP6XTwt9nGV7b/zDp5/+/oVs763POJA+ibO2cgVyhiQJaGx1HTefkb4aPf/QbQeUAPV31lauQM6QJAGNra7jVvfCr3TE6ewSOWsrVyBnSJKAxlbXcavZmFY6mfFe+DTO2soVyBmSJKCx1XXcaj7QgS8snByWGREBmsA8nTMkSUBjq+u41SM9VuJ0dqmctZUrkDMkSUBjq+u48TnQS4N/t3FyWWhYBGgC83TOkCQBja2u44YAPc6yq6sTn/OPMj5ULpGztnIFcoYkCWhsdR23mA80e/HmzZsvLP+86uBDgAZw1lauQM6QJAGNra7jpgDNdzxLOU2nTIAGcNZWrkDOkCQBja2u4+azMT37JN/7PP+W00wiCwI0gLO2cgVyhiQJaGx1Hed0drZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jWycTuXfzjSf5/5t6g3ciJXHWVq5AzpAkAY2truMG09mdu9+d1I4D6dM4aytXIGdIkoDGVtdxAtRWLJe0MyRJQGOr6zjPgdqK5ZJ2hiQJaGx1HSdAbcVySTtDkgQ0trqOE6C2YrmknSFJAhpbXcdtAMqncuZiuaSdIUkCGltdx41v5XyXF5FWYrmknSFJAhpbXcfNn4lEgK7Eckk7Q5IENLa6jhsC9CjLzr/5aSU+lTONs7ZyBXKGJAlobHUdN30mktskyi0n3xWsRYAmME/nDEkS0NjqOu7wSA9HEaDeztrKFcgZkiSgsdV13OGhco4iQL2dtZUrkDMkSUBjq+u4w2ON+3r+pzsHB//d35dffLj84u0v+04OWzciAjSBeTpnSJKAxlbXceOLSBvmof/xnYNCf5d/8dPqi1/8uefksHUjIkATmKdzhiQJaGx1HTcexrR3a2wVzz84+OWXi+f/38HLf1h+9eDglS8XP35w8Mpfu04OWzciAjSBeTpnSJKAxlbXcYP5QAu9nmV7V4bnA31a7m5+e/DqYvHDneKLn94paNpycti6ERGgCczTOUOSBDS2uo4bTGfXV+uK0nIH9LfrrwqKFn/+puvksHUjIkATmKdzhiQJaGx1HfcH6E/vNM93Pihp+rQEacPJYetGRIAmME/nDEkS0NjqOu4/G9MPd1756z/9DwcHv/zHRb47Wh6659+tHEp9F0xZFm5dFEVRrgoC0A9XV+F/Q4BSFIUkf4A+zQcw/XXx/E/5VfgGQLsDmXgI7+2s7fAmkDPksSxobHUdtwTo/C+fftb51tOD8nrRg4NXh/ZAayeHrRsRAZrAPJ0zJElAY6vruDFAT//Dk8Xi7LUsyy60b0r64U6DmQRoTGdt5QrkDEkS0NjqOm4K0KPi0vvhwHSg9cF68RdehY/orK1cgZwhSQIaW13HDQF6XGDzZJZdfHJ6vX1bZ73T+TS/+6ga/8lxoBGctZUrkDMkSUBjq+u4IUAPl+TMMZrPKXJc/H2tB+XO5oOcmbwTKaKztnIFcoYkCWhsdR23mo1phdHu3HY/3MknX1pdhS9vjOe98FGctZUrkDMkSUBjq+u4zXygZ9eLeel7k4M+vVMMA325OPv5I2djiuasrVyBnCFJAhpbXcdtAHoyy24vhmZX/jGfAvR/+rL6YsnPt7v7nwRoAGdt5QrkDEkS0NjqOm5zCH9UnALtnQM1dXJYZkQEaALzdM6QJAGNra7jxheRLuWX33Nydq/CGzu5LDQsAjSBeTpnSJKAxlbXcfNhTLluL+bvZpnbA+YIUG9nbeUK5AxJEtDY6jpuPpB+qUvFhaS92w4+BGgAZ23lCuQMSRLQ2Oo6bn4r57033lv+cfarl75wsFkQoAGctZUrkDMkSUBjq+u4/2xMxk7hVkWAJjBP5wxJEtDY6jpOgNqK5ZJ2hiQJaGx1HTd4qNwbT+pHyw0+VM7UyWGZERGgCczTOUOSBDS2uo4bPBPp3P3uk5G6A+nNnByWGREBmsA8nTMkSUBjq+s4AWorlkvaGZIkoLHVdZznQG3Fckk7Q5IENLa6jjsB9BnPgSZx1lauQM6QJAGNra7jWw/h/7f+Mg//Gx7CJ3HWVq5AzpAkAY2truPbz4F2b3yff8RzoImctZUrkDMkSUBjq+u4wUWkNkFPX+NFpFTO2soVyBmSJKCx1XXcAKAtgj6cZRlnY0rkrK1cgZwhSQIaW13Ht15EOpk1gLk8fO891tjYyWmpQRGgCczTOUOSBDS2uo5vvwrfIGh++J5ddbkEvyBAAzhrK1cgZ0iSgMZW13GDYUw1QfPD971bDiYrJ9cF+yJAE5inc4YkCWhsdR03GQd6WpwHXR2+O85ltyBAAzhrK1cgZ0iSgMZW13GjgfQ5Qf+lz+F74eS+aFcEaALzdM6QJAGNra7jZncinRb3wrsfvhdOPgu3RYAmME/nDEkS0NjqOm54K2dOUI/D98LJa+mWCNAE5umcIUkCGltdx03vhT+97jj8c+3ktXRLBGgC83TOkCQBja2u48aTiXRH1Ns7+SzcFgGawDydMyRJQGOr67j5bExnr/kRlAD1dtZWrkDOkCQBja2u4ya3cvbEe+HTOGsrVyBnSJKAxlbXcQLUViyXtDMkSUBjq+u4wUPl+uJD5dI4aytXIGdIkoDGVtdxPtLDViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVddwQoIcv+c2mvCBAAzhrK1cgZ0iSgMZW13EzgJ5dz247rLzt5LuCtQjQBObpnCFJAhpbXcdNAeo0AVPbyXcFaxGgCczTOUOSBDS2uo6bAXR+d+99h5W3nXxXsBYBmsA8nTMkSUBjq+u44TnQo+yi70lQAtTbWVu5AjlDkgQ0trqOGwL02cdZdv4K5wNdsFzyzpAkAY2truPGF5E4I30plkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6zoH0tmK5pJ0hSQIaW13HCVBbsVzSzpAkAY2truNWAJ0/dnConTyW7YgATWCezhmSJKCx1XXcHKCPXs9Pfp796i2XS/ALAjSAs7ZyBXKGJAlobHUdNwXo/KPV1aOz69kFt5uSCFBvZ23lCuQMSRLQ2Oo6bgrQwyy78OvZufvz32XZRad9UALU21lbuQI5Q5IENLa6jhsC9DjLbpV3xD+cuU0sQoB6O2srVyBnSJKAxlbXcdPp7LJr9ZQiR9klByMC1N9ZW7kCOUOSBDS2uo7bTCZSAvRkxoH0aZy1lSuQMyRJQGOr67jNdHYlQB3ntiNAvZ21lSuQMyRJQGOr6zgBaiuWS9oZkiSgsdV13PQQPr9wVJLz2O0yPAHq7aytXIGcIUkCGltdx43nA71UAXQJU15ESuOsrVyBnCFJAhpbXccNAXoyy64+KeWmkAEAACAASURBVAB6+lrmNjs9AertrK1cgZwhSQIaW13HTQfSH2VZdnm2d+WF5Z/XHHwI0ADO2soVyBmSJKCx1XXc+F74r2bVbKBu/CRA/Z21lSuQMyRJQGOr67j5ZCLPPrm8pOd55wfEE6DeztrKFcgZkiSgsdV1nPOB2orlknaGJAlobHUdNxzGdK/xHLmTGz/jMKYkztrKFcgZkiSgsdV13GYg/dAXFk4Oy4yIAE1gns4ZkiSgsdV13AGgvBc+lbO2cgVyhiQJaGx1Hd8K0M4DOQvxTqQ0ztrKFcgZkiSgsdV1fPse6HEfoJwPNI2ztnIFcoYkCWhsdR3fDtD5H2/evDHbu3Kz0pufO20dAertrK1cgZwhSQIaW13HHc6BOooA9XbWVq5AzpAkAY2truMOw5gcRYB6O2srVyBnSJKAxlbXcQ6ktxXLJe0MSRLQ2Oo6vg2g83s3lzufy/835bQ7SoB6O2srVyBnSJKAxlbX8W0APbuenbvfHcvEcaBpnLWVK5AzJElAY6vrOAFqK5ZL2hmSJKCx1XWc50BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jlgCd/+XTzxxcCifH5QZEgCYwT+cMSRLQ2Oo6bgzQ0//wZLE4ey3LsgtOz5QjQP2dtZUrkDMkSUBjq+u4+UPl8kvvh+4X4QlQf2dt5QrkDEkS0NjqOm4I0OMCmyez7OKT0+tuj5UjQL2dtZUrkDMkSUBjq+u4IUAPizlAj4tHwh9zPtBEztrKFcgZkiSgsdV13HAykbs5OUuM8pEeqZy1lSuQMyRJQGOr67jNdHZn17NLCwI0nbO2cgVyhiQJaGx1HbcB6MmsmIueAE3lrK1cgZwhSdKPvb+UlLeQz4BzwtTRD+GPilOgPAeazJkAFfdOaN3x3t+XY8l03mzJ1HEvIl3KL7/n5ORV+GTOBKi4d0Lrtvf+viBLJvNmi6aOPIxp9TS5+bvZaj/U3slloWERoAnM0zkToGuSyLBkKm+2bOrIA+mXulRcSNpzeignAervTICKeye0JkB3CaCL03tvvLf84+xXL33hYJM7fRdMWRZuXRSlQGuUpN4SSSlIzdmYbDWV387S5umcuQfKPVAZc4dlCFBbTaVc0ubpnAnQBS8iiZg7LMOHytlqMuUSNk/nTIDmmjpJAjnv2jAmPhOpq+mUS9Y8nTMBWmjiJAnkvGsD6QnQriZULlHzdM4EqLh3OmdtHec5UFuxXNLOkCQBja2u4wSorVguaWdIkoDGVtdxAtRWLJe0MyRJQGOr67gNQL+p9NjBiAD1d9ZWrkDOkCQBja2u46YAPX2XF5FWYrmknSFJAhpbXccNAdq+DE+ApnHWVq5AzpAkAY2truOGAD3KsgtvflrpMw6kT+KsrVyBnCFJAhpbXcdNJ1QuHubhJQLU21lbuQI5Q5IENLa6jps+0sNtDtCWk+8K1iJAE5inc4YkCWhsdR23eSaSnwhQb2dt5QrkDEkS0NjqOm71WGMvEaDeztrKFcgZkiSgsdV13PgiktNzkFpOvitYiwBNYJ7OGZIkoLHVddwQoMtd0FsOa285eS7fEAGawDydMyRJQGOr67jhIfy9G1m2d4XzgS5YLnlnSJKAxlbXcdOLSJzOrhLLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR3nbEy2YrmknSFJ4hHbfxJ3zDebABURyyXtTIBaKcBjhDDfbAJURCyXtDMBaqMQD7LEfLP5VE4RsVzSzgSohYI8Sh3zzeZD5UTEckk7E6AWIkA9zB2WIUBtxXJJOxOgFiJAPcwdluE5UFuxXNLOBKiFCFAPc4dlCFBbsVzSzgSojXgRyd3cYRkC1FYsl7QzAWolDmNyNndYhgC1Fcsl7UyA2okD6V3NHZbZOozpjzf74jCmNM7ayhXImQAV907nrK3jWwF6N+uLV+HTOGsrVyBnSJKAxlbX8a2H8IdZdv5yRy8SoEmctZUrkDMkSUBjq+v4VoAeLfc4X/rccYtaTgHWUYoATWCezhmSJKCx1XV8+0WkR69nQRhKgHo7aytXIGdIkoDGVtdxk6vw80evLRG658lQAtTbWVu5AjlDkgQ0trqOmz4T6ZMXCoZ+4WBRObkv2hUBmsA8nTMkSUBjq+u4+TjQZ5/MvBhKgHo7aytXIGdIkoDGVtdxq4H0p+96MJQA9XbWVq5AzpAkAY2truO2dyJ9/S7HgSZz1lauQM6QJAGNra7jtgA9/f2MAE3lrK1cgZwhSQIaW13HrQC6Og2a7b3FWzmTOGsrVyBnSJKAxlbXcXOAri7Eu19FIkC9nbWVK5AzJElAY6vruOkwpmIoqNc4JgLU21lbuQI5Q5IENLa6jhsBdHUzktcoUAI0gLO2cgVyhiQJaGx1Hd8O0NN3A9BzQYAGcNZWrkDOkCQBja2u40aTifjTc0GABnDWVq5AzpAkAY2truMm09ld4ITKDbFc0s6QJAGNra7jnFDZViyXtDMkSUBjq+u4wXPhCdCWWC5pZ0iSgMZW13E+VM5WLJe0MyRJQGOr6zgBaiuWS9oZkiSgsdV1nAC1Fcsl7QxJEtDY6jpOgNqK5ZJ2hiQJaGx1HSdAbcVySTtDkgQ0trqOE6C2YrmknSFJAhpbXccJUFuxXNLOkCQBja2u4wSorVguaWdIkoDGVtdxAtRWLJe0MyRJQGOr6zgBaiuWS9oZkiSgsdV13Aqg88cODrWTx7IdEaAJzNM5Q5IENLa6jpsDNJ9V+dz9s185PRBpQYAGcNZWrkDOkCQBja2u46YAnX+0mkXk7Hp2wWUqEQI0gLO2cgVyhiQJaGx1HTcFaD4t6K9n5+7Pf5dlF532QQlQb2dt5QrkDEkS0NjqOm4I0OMsu7U4u57PY/dwlt12MCJA/Z21lSuQMyRJQGOr67ghQA+za4sSoIuj7JKDEQHq76ytXIGcIUkCGltdx80AOr+7934N0JMZJ1RO46ytXIGcIUkCGltdx80AukJnCdDyD2snh2VGRIAmME/nDEkS0NjqOk6A2orlknaGJAlobHUdNz2Ezy8cleQ8drsMT4B6O2srVyBnSJKAxlbXccOLSMWFoxVAlzDlRaQ0ztrKFcgZkiSgsdV13BCgJ7Ps6pMCoKevZfkFJQcnl4WGRYAmME/nDEkS0NjqOm46kP4oy7LLs70rLyz/vObgQ4AGcNZWrkDOkCQBja2u48b3wn81q54K78ZPAtTfWVu5AjlDkgQ0trqOm08m8uyTy0t6nn/pCweXwslxuQERoAnM0zlDkgQ0trqOcz5QW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13FzgP7l01qfcRxoEmdt5QrkDEkS0NjqOm4K0PU1pGJaUBcnh2VGRIAmME/nDEkS0NjqOm4+nR0BuhLLJe0MSRLQ2Oo6bnwr595739RyejQSAertrK1cgZwhSQIaW13HTScTcZtEueXku4K1CNAE5umcIUkCGltdx21mY/ITAertrK1cgZwhSQIaW13HTQ/hCdBKLJe0MyRJQGOr67jxbEw8hC/Fckk7Q5IENLa6jhsC9Oy62xRMTSfP5RsiQBOYp3OGJAlobHUdNx0Heno9u3Cz0hscSJ/EWVu5AjlDkgQ0trqOmwL0I44DLcVySTtDkgQ0trqOG58DJUBLsVzSzpAkkYi9v9Swd3TrManruMVAepfj9qaT3+JNEaAJzNM5E6BxtL8/RlDMNzvqOFDva0gEqL+ztnIFciZAo2h/f5SgmG82B9KLiOWSdiZAY2h/f5ygmG82B9KLiOWSdiZAY4gA7Zs7LGN8EcnxSUgNJ98VrEWAJjBP50yAxhAB2jd3WMYQoPO7e7c2r+iHO6/8tfjL8w/vHBy8/WXfyWHrRkSAJjBP50yAxhAB2jd3WMbwEP7ejSzbu7JhIP3zDw5WAP3pnYNcv/hzz8lh60ZEgCYwT+dMgEYRLyL1zB2WMZ7Obss40G8PSoA+OHjly8WPFU6bTg5bNyICNIF5OmcCNI44jKlr7rBMIID+cKcE6A93in3Pn955+Q9dJ4etGxEBmsA8nTMBGkkcSN8xd1gmzFM5lwfw/251DvTbg1eL73x78Juuk9OaB0WAJjBP50yAinunc9bW8TAAfXDwankR6cHBb4vvPC1B2nByWvOgCNAE5umcIUkCGltdx4MA9Ony8H0F0OcflIfu9UX5pUOp74Ipy8Kti6IoylXbADq/l19zX/6/qc5V+OKEJwFKURSatgH07Hp+yWjzRaQH+fnOHkC7A5l4CO/trO3wJpAz5LEsaGx1HQ8A0G+L6++je6C1k8PWjYgATWCezhmSJKCx1XXc/xzoD3cKZhKg8Z21lSuQMyRJQGOr67g/QL89qLU8audV+IjO2soVyBmSJKCx1XXcEKCHL30xtoY2QKvxn0jjQEfHI4d21lauQM6QJAGNra7jxncibXuscXnMDngn0vgdcaGdtZUrkDMkSUBjq+t4sAmVS4A+/+Dgl1j3wm+YkyG0s7ZyBXKGJAlobHUdN51QeesjPaqrRj+Czca0aVaw0M7ayhXIGZIkoLHVddx4QuWLoydBV6ovu//44ZKfb3f3PwnQAM7ayhXIGZIkoLHVddwQoM8+zrLzm+YDNXByWGZEBKi8MD9TBKi0s7aOh5oP1MDJYZkREaDywvxMEaDSzto6ToDaiheRpJ0hSQIaW13Hw0xnZ+QUblWTAiiHMcV2hiQJaGx1HSdAbcWB9NLOkCQBja2u44YAfeyw6q6T/yoqTQygYs7ayhXIGZIkoLHVddwAoPOPZlmWnb/lcum96eS3eFMEaALzdM6QJAGNra7j2wF6MisvHV1wuXTUcPJauiUCNIF5OmdIkoDGVtfxrQAtLsCf/29fWP7/otc+KAHq7aytXIGcIUkCGltdx7cC9CjLits4Hy13RLdNKLLZyWfhtgjQBObpnCFJAhpbXce3PhPpbsXNJUkvOW1W5eSzcFsEaALzdM6QJAGNra7jBgAt5xE5mfkdwxOg3s7ayhXIGZIkoLHVddzsmUjtv7mJAPV21lauQM6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXLF0sisVpgkAY2truMGAO2LEyqncdZWLjuNzauKSRLQ2Oo6ToDaiuWKo9GZ/TFJAhpbXce33ol072ZffKhcGmdt5bLR+LOlMEkCGltdxzkjva1YrigiQLvWkLHVdZwAtRXLFUUEaNcaMra6jhOgtmK5oogA7VpDxlbXcQLUVixXHPEiUscaMra6jhOgtmK5IonDmNrWkLHVdZwAtRXLFUscSN+yhoytruMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSZIdjz1yvltfxwlQW+kkyVhhzc29lvZy3mmSjFvvcuyxERe7DNBnn1zOsr3Lbz12cCmcHJcbEAFqq9HCmpv7LOylnSbJBusdjj065neHAXpUz8V0zcFmQYAGcHa2Hi+subnHsn7aZZJsst7d2ON3ne0uQHN+nr9y88YLzgQlQL2dXa03FNbc3H1RT+0wSTZa725sQICezLKLXxR/O72b7b3vYESA+jsToOLeCa13NzYgQA+zi9UcoPO72SUHIwLU35kAFfdOaL27sfEAOr/b2Os8mV3khMpJnAlQce+E1jscG+4i0tn1xlM8Wl9YODksMyIC1FK8iOTqndB6l2OjDWMiQNdSSRIOY3L0Tmi907HBBtLP72a36y+OMx7Cp3HmQHpx74TWkLF3FKC8iLQWyyXtDEkS0NjqOm4+jOnC58Xfvn6Nw5hSOWsrVyBnSJKAxlbXcZuB9Nnly5fdb0UiQL2dtZUrkLPfqV8/b6+l/awJUHFzh2WMb+V8OCvv5Ny75WCzIEADOGsrVyBnz8EHXt4+C/uJAJU3d1jGfDKR+aMbyz3QK++5XEAqnByXGxABmsA8nbPv8Fcfb49lPUWAyps7LMPp7GzFckk7O1rrvn2AAE1g7rCM4TCme2+sdzxPbvyMw5iSOGsrVyBnAlTcO52zto5zIL2tWC5pZwJU3Duds7aOOwD0ZEaApnHWVq5AzgSouHc6Z20d3wrQs+tZT7wTKY2ztnIFcuZFJHHvdM7aOr59D/S4D9DbvR8ycXJZaFgEaALzdM4cxiTunc5ZW8e3A3T+x5s3b8z2rtys9ObnTltHgHo7aytXIGcOpBfT92aKtwHqOu5wDtRRBKi3s7ZyBXKG3BUTjG1ITRGQquu4wzAmRxGg3s7ayhXImQCNpFEkbrGOyFF1HedAelthkgQ09u4CdCMADa0jQFRdxwlQW2GSBDT2DgLUhHp21iEpqq7jBKitMEkCGnvHAGq6y2hvHQqi6jpOgNoKkySgsXcHoFaEc7QOwFB1HSdAbYVJEtDYuwFQ691DD2tPiKrrOAFqK0ySgMZWD1C3Q2s/ax+Equs4AWorTJKAxlYNUPfzkv6xnZ21dZwAtRUmSUBjKwao136gn3XT33ID1HXc9KmcL33hsPK2k+8K1iJAE5incyZALeV9MSdYbPuTB9o6bnorp9sEIi0n3xWsRYAmME/nTIBaKMh4ooCxLTdFXcd5L7ytMEkCGlsZQEMNaA8a2+76v7aOG94Lf9ftWfAtJ98VrOUFUL0znKkrVyBnAtRE4e4GCh7bZgSqto4bngM9yi76ngSdCEAVz7GrrlyBnAnQ7QpIz0WM2Kbbp67jhgB99nGWna+nBHWammkaANX8lAd15QrkTIBuU0h4Ft4B17WWyVaq67jxRaSm9D4TKcBzcjBJAhp7+gANeeS+9g66trW2b6q6jhOgtsIkCWjsqQM0Cj5jxt62teo6DjaQngB1Nk/nTICOKAY7V94xVlpp4zar6zgBaitMkoDGnixAI+16Vt5xVltq03ar67gVQOePHRxqJ49lO+JFpATm6ZwJ0K7i4jN67A3brq7j5gB99Hp+8vPsV285Ph3JGaB93nEYUwLzdM4EaFtR2bnyjrr2xYarSeo6bgrQ+Uerq0dn17MLbjcluQJ0gHgcSJ/APJ0zAVor9q5n5R15/YtRhKrruClAD7Pswq9n5+7Pf5dlF532QR0BOnTMzVs5E5incyZASwnhUyr2UBR1HTcE6HGW3SrviH84c5tYxA2gg1d9CNAE5umcCdBCMuxcecvYDFyQV9dx0+nssmv1lCJH2SUHIwLU31lbuQI5E6ALUXwKxu6FUtdxm8lESoCezAQH0hOgDWdt5QrkTIDK4lMydjeYuo7bTGdXAtRxbjsC1NtZW7kCOWMDVOzEZ9Nb0KudTV3Hpw5QXkRqOGsrVyBnZICmwKd07GY+dR03PYTPLxyV5Dx2uww/lWFMvsIkCWjsxABNwM6Vt6xdI6a6jhvPB3qpAugSpoIXkRahB9J7C5MkoLFTWqfY9ay8pQ3rqOo6bgjQk1l29UkB0NPXMrfZ6adxK6e/MEkCGjuddUJ8JoldplXXcdOB9EdZll2e7V15YfnnNQcfAjSAs7ZyBXIGBGhKei4SxS4Sq+u48b3wX82q2UDd+EmA+jtrK1cgZziAFvjEjK2u4+aTiTz75PKSnuedHxBPgHo7aytXIGcwkpR7n2ixFyVB01gXigtQXxGg3s7ayhXIGYckzUFLQLHX0nfmwnAY073Gc+RObvxM70Pl/IVJEtDYotbtMZ8wsVtKStDYA+mHvrBwclhmRARoAvN0zhgkmdJNjSnf7IQEFQKo673w3wVTloVbF0XFUD562fynvy+vG1Gr1yL1NphrK0A7D+QsJHon0oC4B5rAPJ2zxl0xqwcfDI75VBnb3/m7hIfxcfZAj/sAFZwPtKWqkwRoAvN0zgpJYvPorZExnxpjB3CuZgBIYu6wzHaAzv948+aN2d6Vm5Xe/Nxp6/wBWneSAE1gns5ZH0ksHv46OmReYewQzitrPVMAOJwDdZQ3QNedJEATmKdz1kcSY4BuuONIYewQzo1J/OTNHZZxGMbkKF+ANkpJgCYwT+esjyRmAN18u7vC2CGcK+sUN7NGH0if9LnwBCgBKu/ttpgJQLfNFqIwdgjntbWOaVAVPBe+FAFKgMp7Oy63lZ/bd7A0xg7g3LSWJmhMgKZ7LnwlApQAlfd2XXAzP02OT1XG9nduWSt4ksnknwu/Fi8iEaDi3s5LbuCn2ek9nbG9ndvWsgSNCNBkz4VvisOYklmjxg5vbTxR8m7FNnbuWE/+YaRTfy58SxxIn06YsUNbW8wzv0uxLZy71pIEjTiMKdlz4QeVZcpmHAjkTICKewddm9XQnN2JbeXcsxb8qMceSJ/kscZDKgCa6o7ZKZVL0jydc8/a4jZzX++A67JsLAFaSu6DDgTQ9eSJKkbbBnImQAtZTdTh6R1qRfZVJUAriX3IYx7Cp3su/ICqc6BJEDqtcsmZp3PuWNtM1OHtHWY1LjUlQNcS+oxHvIiU8rnwfbUuIklDdGrlkjJP59y2tpioI4B3iJW49ZMAbUjmAx4RoJN+Lrzs0fzkyiVkns5ZN0Bdi0mANiXy4Y45kH7qz4WXg+j0yiVjns5ZM0DdK0mAtiRB0Kj3wmt4LrwIQidYLhHzdM5KAer5G50AbWui+0Y791z46DuiUyyXhHk6Z5UXkbx7SIB2FJ+gcQHqK7FnIkVG6CTLJWCezlnhMKYABSRAu9r8mgboBDRAO69fPIhOs1zxzdM5axtIH6Z6BGhPm17WEL9V4wL0L5/W+izBONDmyzMA0P7rF2tHdKLlim6ezlkXSUK1TlnsUM6brUdf2SDndWICdH0NqZgW1MXJYZm1Wi9PH6Ajr18MiE62XJHN0zlrIkm4uqmKHc55i/XIixvmymJEgLafbSwP0PXLk/+/B9ANr19whE63XHHN0znrIUnIqimKHdJ5m/Xw6zt1gM7vZnvvfVPL6dFIPgDdb8sGoLmC7ohOuFxRzdM5ayFJ2N/UamKHdd5qPfgSTx2gZ9fdJlFuOXks6wnQoAidcrlimqdzVkGS4OeKdMQO7rzdeuhVnj5A0z4XvgfQ/bF/37SWMBCddLkimqdzVkCSCNcrNcSO4GwC0A0E9TN3WMb0EH7aADV9/ULsiE66XBHN0zlPniRxRntMPnYUZwPrDQT1NHdYxng2pqSH8IttALV4/XwROu1yxTNP5zxxkkQacDz12JGcDV/x/jf9+RkToGfX3aZgajp5Lb0NoFavn9d+6MTLFc08nfOUSRLvprdJx47nbGQd6yV3WMZ0HOjp9ezCzUpvJBtIv/zf9wMXkazlgdCplyuWeTrn6ZIk5j3DE44d09n4pEkMc4dlTAH6UdpxoLX2ly9dAIDmcjwhOvlyRTJP5zxVksSccWHCseM6G1rHOWvisIzxOdCJALQobajHGjshdPrlimOeznmaJImLz8nGju1sMe4hvLnDMhYD6V2O25tOfouvlL9s4QBardLqvVBQrijm6ZynSJLY+Jxo7PjOxtYTmebCdByo9zWkUABdhAao7Y6ohnLFME/nPDmSxDz1ubaeXGwRZ5tbF4KbOyyjYyB9rSgAtUOoinJFME/nPDGSiOBzerGFnC2sg78JuzuQvlbxmoUHaLluo8+FjnKFN0/nPCmSyNBzMbXYYs421qHfiKgXkRyfhNRw8l3BojrvEQmghh8OJeUKbp7OeUIkEcPntGILOltO3xLW3GEZQ4DO7+7dclh7y8lz+VyrFywaQI2O5bWUK7R5OufJkEQQn1OKLepsCdDkU/0aHsLfu5Fle1dSDqQvFB2gBgjVUq7Q5umcp0ESoVOfa+tpxJZ2trMO+47EvIg0iXGg3wsAtDIafWfUlCuweTrnKZBEGp8TiS3vbGkd9E3ZSYA273EvX6z4AN20I6qnXGHN0zmnJ4k4PReTiJ3C2dY65BsT81ZOf7kBtDXLkiBAxxGqqFxBzdM5pyZJCnxOIHYaZ2vrgO/NDgK0Pc9n9VJJAHTlO/DZ0VSukObpnJOSRP7YvbImQM0U7u3ZPYC2Z5r/XhCgtXFvR1RVuQKap3NOGDsZPglQcykB6LNPLmfZ3uW3nJ4otwgD0PLbkgDd7x7L6ypXOPN0zsms09FzQYBaKNSbFBWg6/mYHIfUBwNoiPlALZ2bnyVl5Qpmns45kXVSfBKgNkr4wF1TgOb8PH/l5o0XnAkaCqAjM9IHVheg68+TtnKFMk/nnMR69XanhBgBaq7JA/Rkll38ovjb6d3MbWYm74tIK36Vj/QQI2jjW3azNoUXASql79P/tiRAbRTmQxkRoIfZxeruo/nd7JKDkf8wppqfBUCFCNr+XlqEEqASar3HBKi0s6N1kM9kzNmYGnudJ7OLgrdydgYxyQF07DF13yVDKAEaX53fkASotLOrdYiPZMw7kRo3HzlODup7K+f30gAd0XfJLi8QoJHVP7wQsB57nCwBaqUQn8hdB2j+/0kANNGxPAEaVUPvaHzroTNFK2sC1EoBPo0xD+Gz2/UXx5nkIXytehCTzEWkUTVvj46F0OHPFAEaUcNvZnTrgWuVlTUBaif/D+MuXkSqVb88MsOYxtV8lSMhdOQzRYBG09gbGdu6P1pubU2AWsr7sxh3GNOFz4u/ff2a5DCmtdYvjshA+nG1X+UYO6JjnykCNJLG30ECVNrZx3rCAF3diHT58mX3W5GCAVRmMpFRdV/l4Agd/VARoDG08d0jQKWdvaw9P4VRb+V8OCvv5HR9tocfQL+fLEALhUQoAdpxjmm95ZcfASrt7Gft9yGMO5nI/NGN5R7olfdcLiAVTo7LrdR6ZaYH0JAIJUA7zrGsDQ4deBFJ2tnTesIA9ZUXQPe//77RsSkCNNyxPAHacY5ibfZucRiTtLMvQH0+frsL0HxWzkbLpgnQYAjlRaS2cwRr0/eJA+mlnX2tfT59cQH67wbNMAAAIABJREFUl09rfSY8DnTFz3XPpgrQQgEQOsGzYpo/Uz2Zv0W8lVPa2dt6ogD9apbooXKLnCcrftZMmTRAQ+yITu+smOrPVFs27w0BKu3sD1D3z11EgB6nfKxxtQOqBaCxblMiQAPI7n0hQKWd/a3dP3ZRb+Xce++bWk5P9XAHaM1PNQAtFByhBKifHH6tEaDSzgGsJwjQs+uNe+Ed5QPQfY0ANRonY+VMgHrI6aiAAJV2DgFQ10+c0GxMjnIGaG8HVA1AAx/LE6DucnwXCFBp5zBvtqO5wzKmh/DpAJoXv3NZWg9AcwWDKAHqKufXnwCVdg5i7fhhi3gR6SjdIXw9j/L6W7oAugh1LE+AOsnntSdApZ0DAVTs16UhQM+uu03B1HRyXG7gtVAH0Fz+DCVAreW790+ASjsHO+HtYu6wjOk40NPr2YWbld4QHEg/9FKoBGiATzMBaif/HX8CVNo5kPXkAPpRwnGgPSkF6MLzI02A2ijMWRPP5X2sCVAfSV0yND4HSoCWCnCrhONnmwA1Vbjrdt5rcLcmQL3k8gFzsLEYSO86j13l5Ld4U7oB6vz5JkDNFHDoGAEq7RzOekoADXANiQDtyGFUNwFqoLD3LoRakYM1Aeon+xbs6ED6vnYCoPY7ogToFoWfgoAAlXYOaG1/iOdgMv2B9APaEYDafuIJ0I0Kj08CVN456MfL1tzBw/giktuT5JpOvitYa2cAWsj4c0+AblBwdq68g6/R3JoA9ZVtISICdH7X9VlyayfP5RvaLYAaI5QAHVaMXc/KO8I6Ta0JUG9NBqDzezeybO9KioH0Q9o1gOYyoAABOqSI+CRA5Z3DAjT+3FvG09lxHGipaOX6/vstLCBAu4rJzpV3vFVvtSZA/WXXDQJUQnHLtYmhBGhLUXc9K++YK99iTYD6y64eMW/l9BcBaqoxMhCgtSTgWXjHNthgTYAGkFVHCFAJiZRr8GieAC0lRM8FASrvHNp6IgA9fOkLh5W3nXxXsNbuAzRXj6EEqNyuZ+Ut5DNkTYAGUeRHCGp4JlJPGADN1eIFOkC3XmWL4C3m1LcmQMPI4lY/h7XzVk5bSZdrTQ1sgEqzc+Uta9eyJkADaQIAnd/lZCKVUpQrwb5XVyk/U+nCE6DSzlHuUzE1d1i58a2cF31PghKgfkoMUczYBKi0cwzrCQD02cdZdp53IuVKWq50OEkQG3q/mwANJ+PZehzWzYH0tkperjRcEY7dCIlJEtDYcVIToG1hA7SQOEXlYnejYZIENHYkgJp9SjiQXkLTKZcoRGViD0XCJAlo7EipzT4iBKiEJlYuKYrK3cHaDYJJEtDYsVJPA6Dzx8Pf/+d/e3Dw8ttfrr54/uGdg4Pqi6aTw9aNiABtSwKiUWNvDIBJEtDY0QBq8tGIC9BHr+cnP89+9VbvEvyfDgq9/If8i5/eKb74xZ97Tg5bNyICdEDffx+Vo9Fib91qTJKAxo6WOjVA5x+trh6dXc8udC4hPT14+e8Xix8/WEHzwcErX+ZfvPLXrpPD1o2IAB1RRIjGuMXObGMxSTKp2PtLCTlHA6jJM3Mc1msK0MMsu/Dr2bn7899l2cXWPujzDw5+m/+53PVc/vnDnQKjP72z2h9tOjls3YgI0M2KwdGAsb9va6vzhEgiaT2d2Pv7YgSNl9rgoxARoMdZdqu8I/7hrD2xyE/vlIfrDw5+s1h8e/Bq8cW3+RdtJ4etGxEBul12kDIw91+F40ZNiCQ7sS9m4t36an9fjqAxAbr9V7XDak2ns8ufyllOKXKUXRr8mQKgD1a7o8vj+le7Tg5bNyIC1FidvT13kvrF9tmI6ZBkR/bFDLybX+zvCxI0YurtxYs9mUgJ0JPZ4ED64qj9+QflofsPd6qToH9T6btgyrJw64JQF6NLpfEVcY2niiSpt0NUa4Cm3hJPRamfzXR2JUBH5rYrDt4J0GlLhqSpeB1bO4MSK+1O6hg1DAbQp8UwpgZAuwOZeAjv7RzvQs72g+st3tbrM9dkjmV35mB2u3fzix1KvaWPMQ/h8wtHJTmPO5fhCz2983J+8nNgD7R2cti6ERGg4TRCPj8F3cLJkGSXULLFu/XVblxEyrWlmVHnA71UAXQJ0/5FpG/LYfQEaEzn6NZTYWZL0yMJGEB36NJZMoCezLKrTwqAnr6W9Wen/9NBNeyTV+EjOif8SIHGHiTJLuyLbfHufL0zg7e23LLhsEbTgfRHWZZdnu1deWH557XOvz1/cPDL6oRnNf6T40AjOBOg4t6dr3dnX2yzdzrnyNYbCRr1XvivZtVsoF1+5ndv1ofrvBMpojMBKu7d/cbO7Itt9k7nHNs6GUAXzz65vKTn+f4D4r9t3vf+/IODX/Je+EjOBKi4d0JryNjxAeo+0mRQ/vOBltMv5cpPe/7I2ZiiOROg4t4JrSFjC1wo3WDusDp/gD49aAF08eOHy7+93d3/JEADOBOg4t4JrSFjS4w0GTd3WJ0NQL+pNDKv8hYnl4WGRYAmME/nDEkS0NgCqdMAdP4uHyq30k6Xa4N5OmdIkoDGlgDoKEEjArT9WE4CNI0zASrundAaMrZE6vFnHziszPhOpOz8m59W+qx/K6eBk8MyIyJAE5inc4YkCWhskdRjBI16L/zwHKA2Tr4rWIsATWCezhmSJKCxZVKLA/Tsev/2TWsn3xWsRYAmME/nDEkS0NhCAB0maEyAOp32bDv5rmAtAjSBeTpnSJKAxhZKLQ3Q1Yz0fiJAvZ0JUHHvhNaQsaUAOkjQqBeRenfAWzv5rmAtAjSBeTpnSJKAxpZKLQ3Qs+t7txzW3nLyXL4hAjSBeTpnSJKAxhYD6BBBYwB0fu9modezbO/KzVJvcBhTEmcCVNw7oTVkbLHUUgBtj6BPPpB+NZsYAZrAPJ0zJElAY8sBdICguw/Qcj5bAjSBeTpnSJKAxpZLLQTQcAoA0OqJCgRoAvN0zpAkAY0tCNA+QXcdoPsEaDphxiZApZ3lrCUBOr/XuG50cuNnaS4iEaDJrFFjE6DSzoIA7RFU6E4kx9uSCFBvZwJU3DuhNWRsydSpAHoyI0DTOBOg4t4JrSFjiwK0S9AoAB26DH8x0ThQAjSdMGMToNLOktYiAF0c9wF628GIw5j8nQlQce+E1pCxZQHaIWgcgM7/ePPmjdn6NqSbb37u4MOB9AGcCVBx74TWkLFlU4sANBens1sLpVxd83TOkCQBjS0M0DZBhYYxOYoA9XYmQMW9E1pDxhZO3Sborg+kr0WAJjBP5wxJEtDY0qlbBCVAJYRTrrZ5OmcB69W59QHv+NZjIkBFRIBKC6hcLfN0zvGty9EdA97RrUdFgIqouQtKgErI4x0e288xdSZA46gaXzzgHdt6XASojAhQYbm/w6P7OabOBGgU1Xe49d8bAlTaWR6ga4ISoBJyfofH93NMnQnQKCJAe97pnOWtCVBZub7DGz6mps4EaBQRoD3vdM4JAFoTlACVEAEq7UyAinunc05gTYCKigCVduZFJHHvdM4pAFoRNCJAn33TlIMPAUqAujpzGJO4dzrnFNYVQWPeC9+Zj+n8W9a3dqIDlBeRXJ05kF7cO50zCECzbO99WyeHrRuRToDGGMbkObLUxlzGZsgZkiSgsXcUoPO/fLxE5huffvrpvVm299anv3/d/tnGBGj4gfS+SLYxF3EZdIYkCWjsRABdETTiOdDlLui18q8PC3Serr9h6mS9baPSClBf566190kBG3MJk2FnSJKAxk6TOj5ADxu4PMouLfKJ6i0f7EGAejt3rP0vS9mYC3iMOEOSBDT2jgJ0frdxyvNklqPT+tFyBKi3MwEq7p3QGjJ2otQrgsa8iNR7rLH1JPUEqLczASrundAaMnaq1AVBYwK0tQdKgKZxJkDFvRNaQ8ZOljonaNRzoJc6f+c5UHlnXkQS905oDRlb3VhnQ4AeZ9nVFS/nHxWPNX4441V4cWcOYxL3TmgNGXtXAbrc7cyyF2/evHl5+eelYmC97Uh6AtTbmQPpxb0TWkPG3lmA5juepX7+JAfo3m1bJ4etGxEBmsA8nTMkSUBjq+u4+WxMzz65XNwE/3iRP+b4P/JeeHlnbeUK5AxJEtDY6jrO6exsxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdx20AWs8H+tjBiAD1d9ZWrkDOkCQBja2u46YAPX23MZWd7URMKyeHZUZEgCYwT+cMSRLQ2Oo6bj4bEwG6EstlKd+hVpgkAY2truOGAD3KsgtvflrpM+tL8AsCNICztnIV8h7sj0kS0NjqOm46G1PjVk5HEaDeztrKlcv/dlNMkoDGVtdxh8lEHEWAejtrK9ciyIQnmCQBja2u4w7T2TmKAPV21lauBQHqYQ0ZW13HHSZUdhQB6u2srVwLAtTDGjK2uo4bX0SynHtpwMl3BWsRoAnMu98wgyIB6mwNGXtaHTeQIUCXu6C3HNbecvJcviECNIF552tTKvIikqs1ZOxJddxEhofw925k2d6Vm6Xe4DCmJM4TKpc5FzmMydEaMvaUOm4k04tIGQfSl2K5FnZH5hxI72YNGXtCHTcTAWorlmsh+zgmTJKAxp5Qx83E2ZhsxXItCFAJa8jYE+q4mQhQW7FcCwJUwhoy9oQ6biYC1FYsVy7JBypDkgQ09pQ6bqRtAJ3fy6+5L//fFK/Cp3GeUrkEH6gMSRLQ2JPquIm2AfTsen7JiBeR1mK5VpJ7oDIkSUBjT6vjBiJAbcVySTtDkgQ0trqO8xyorVguaWdIkoDGVtdxAtRWLJe0MyRJQGOr6zgBaiuWS9oZkiSgsdV13OAqfF+8Cp/GWVu5AjlDkgQ0trqOG1xE6osXkdI4aytXIGdIkoDGVtdxAtRWLJe0MyRJQGOr6zjPgdqK5ZJ2hiQJaGx1HSdAbcVySTtDkgQ0trqOWwF0/tjBoXbyWLYjAjSBeTpnSJKAxlbXcXOAPno9P/l59qu3XC7BLwjQAM7ayhXIGZIkoLHVddwUoPOPVlePzq5nF9yecEyAejtrK1cgZ0iSgMYWSD0+iUNMgB5m2YVfz87dn/8uyy467YMSoN7OBKi4d0JryNjxU2+YRiwiQI+z7Nbi7Ho+funhLLvtYESA+jsToOLeCa0hY0dPvWki24gAPcyfC78C6OIou+RgRID6OxOg4t4JrSFjx0698VEK8QA6v7v3fg3QkxkH0qdxJkDFvRNaQ8beUYCu0FkCtPzD2slhmRERoAnM0zlDkgQ0NgE67uSwzIgI0ATm6ZwhSQIae0cBOr+bXzgqyXnsdhmeAPV2JkDFvRNaQ8be1YtIxYWjFUCXMOVFpDTOBKi4d0JryNi7OozpZJZdfVIA9PS1LL+g5ODkstCwCNAE5umcIUkCGntnB9IfZVl2ebZ35YXln9ccfAjQAM4EqLh3QmvI2Oo6bnwv/FezajZQN34SoP7O2soVyBmSJKCx1XXcfDKRZ59cXtLz/EtfOLgUTo7LDYgATWCezhmSJKCx1XWc84HaiuWSdoYkCWhsdR3nfKC2YrmknSFJAhpbXccF5wP9LpiyLNy6KIqiXMX5QG3F387SzpC7YqCx1XWc84HaiuWSdoYkCWhsdR3nfKC2YrmknSFJAhpbXcc5H6itWC5pZ0iSgMZW13HOB2orlkvaGZIkoLHVdZzT2dmK5ZJ2hiQJaGx1HSdAbcVySTtDkgQ0trqOcz5QW7Fc0s6QJAGNra7jnA/UViyXtDMkSUBjq+s45wO1Fcsl7QxJEtDY6jrO+UBtxXJJO0OSBDS2uo5zPlBbsVzSzpAkAY2truOcD9RWLJe0MyRJQGOr6zjnA7UVyyXtDEkS0NjqOk6A2orlknaGJAlobHUd3wbQ+V8+/cxxAtCuU5C1FCJAE5inc4YkCWhsdR3fBtCz65nTfUcDTkHWUogATWCezhmSJKCx1XXcFKBnr19+0Q+kBKi3s7ZyBXKGJAlobHUdNwao954oAertrK1cgZwhSQIaW13HCVBbsVzSzpAkAY2truMEqK1YLmlnSJKAxlbXcQLUViyXtDMkSUBjq+s4AWorlkvaGZIkoLHVdZwAtRXLJe0MSRLQ2Oo6ToDaiuWSdoYkCWhsdR0nQG3Fckk7Q5IENLa6jhOgtmK5pJ0hSQIaW13HDQDaFx8ql8ZZW7kCOUOSBDS2uo4ToLZiuaSdIUkCGltdx7fOxnTvZl9v8KmcSZy1lSuQMyRJQGOr6zjnA7UVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOh4eoM8/vHNw8PaXfSfvNdciQBOYp3OGJAlobHUdDw7Qn945yPWLP/ecfNe8FgGawDydMyRJQGOr63hwgD44eOXLxY8fHLzy166T+zr3l2p+TYAmME/nDEkS0NjqOh4aoD/cKfY9f3rn5T90nZzXub/fISgBmsA8nTMkSUBjq+t4aIB+e/Bq+edvuk6uq9zf7xKUAE1gns4ZkiSgsdV1PDRAHxz8tvjzaQnShpPjGvf3ewQlQBOYp3OGJAlobHUdDwzQ5x+Uh+4/3KlOgv5Npe/ctAZo/a0sc1wXRVFUQBGgFEVRjooH0O5AJh7CeztrO7wJ5Ax5LAsaW13H4++B1k6u6+RFpMpZW7kCOUOSBDS2uo4rACiHMVXO2soVyBmSJKCx1XV8+lfhFxxIXzlrK1cgZ0iSgMZW1/Hw40B/0/qz4eS55oYI0ATm6ZwhSQIaW13HNdyJ1BMBmsA8nTMkSUBjq+t4aIA+/+Dgl8Hvhe+KAE1gns4ZkiSgsdV1PPhkIj9yNqZoztrKFcgZkiSgsdV1PPx8oD9+uOTn2939TwI0gLO2cgVyhiQJaGx1HeeM9LZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwlQW7Fc0s6QJAGNra7jBKitWC5pZ0iSgMZW13EC1FYsl7QzJElAY6vrOAFqK5ZL2hmSJKCx1XWcALUVyyXtDEkS0NjqOk6A2orlknaGJAlobHUdJ0BtxXJJO0OSBDS2uo4ToLZiuaSdIUkCGltdxwUBSlEUtWsiQCmKohwlBdCA6m40iBgbSZix1acmQKcrxkYSZmz1qQnQ6YqxkYQZW31qAnS6YmwkYcZWn5oAna4YG0mYsdWnJkCnK8ZGEmZs9akJ0OmKsZGEGVt9ahUApSiKmqIIUIqiKEcRoBRFUY4iQCmKohxFgFIURTmKAKUoinIUAUpRFOUoApSiKMpR0wfo8w/vHBy8/WXqzZDRT++8Wv6tFXunX4N//rcHBy8PJt3t2P9mGft//uvqC5zYS/1w55VV7F1IPXmA/vTOQa5f/Dn1hojowUEJ0FbsnX4N/lRkO3j5D/kXOLG/XcX+ZT/pTsde6vkHByuA7kTqyQP0wcErXy5+rF7z3dbzBwcVQFuxd/k1eHrw8t8v8nDFRwcm9g93itj/ZvV+w8TOtfzVsYq2E6mnDtAf7hQfrJ/eWe2h7LTyg7oSoK3Yu/waLHdHfpv/udwB+S1Q7CUvfpP/scqIE3uR5ysBuhuppw7Qb0uifLsq3C5r+Zv57/6pjtuIvcuvwU/vlAdtD3pJdzl2pVV8pNjL35j/bnUOdDdSTx2gD1Y7KMsjvVe3/KR6ffvLf6xjtmJDvAYFQOFir66nIMV+cPBqeRFpN1JPHKDPPyh36esrd7utsj+t2BCvQXHsBhf7n+7k3ECK/XR5+L5KtiOpCdBJCRagxSEcWOwHBwcv/+MCKnbxe5IAlVPjhVU3wMFFfYD+4s8Ir8HTYhgTVuzn//f/eOfg5f8FKnZxoqYHUM2p9QBU2W8mN4HugT6983J+Cgwt9mLxz/kxPE7sb4vr79wDlZPeF9ZNmAD9thxGDxY7V35OECb2D3eKbASooNRenXMT5FX4Px1Ug/+gYhcqgIESu7z7qrzjaDdSTx2g1bgwdePD3PS0MxyuHCO3y6/B8wfl/YwLoNjV/QMrgKLEbgN0N1JPHaBq71BwUwXQ3bhLw0gPGnfv4cR+UB9qvIoUu1B5mL4bqacO0OWv6l+qvEfWTRVAW7F3+jX4tpkKJ/YPdw7+7q+L538qRx+gxC5UAnQ3Uk8doIsflc7S4qb6FFAr9g6/BuUkPAflLAAosfN3ejUJVXEkjxM7V3WhaCdSTx6gix8/XL6ubyv7veSq9Tn0VuzdfQ2erk+LFclBYi/1Y3MaVJzYi8aV9l1IPX2AUhRFTVQEKEVRlKMIUIqiKEcRoBRFUY4iQCmKohxFgFIURTmKAKUoinIUAUpRFOUoApSiKMpRBChFUZSjCFCKoihHEaAURVGOIkApiqIcRYDC6DBr61rxvYtPvFZ6lJ2731jP/JNZlp17v/4zusSMRp0HtuBkdmnTkkYv+vxu8cJSExcBCiMJgBYey+9Uf0ZXfKOHPxt+hcajnl3fvEFmL/rJzPOtoSREgMJIAKAns+xfNP+MrvhGY6/QhqiHxUtrv8rej21eDTUFEaBgmt/NNh5fWuqotfN1nGW3m39GV3yjMdqNRw2163gy40H89EWAgikyQPfeb/4ZXfGNxgE6EnX5Agdi+mHQd4qKIgIUTASonawBehzslOzJTOhVpNxFgIKpDdAVHorvPXoty/auLr969HqWZS99Uf7E6buz5ffrL2vlP7X88eY50KPVudW9G+Wf73cXz3fOcpvzvX85u55dm3/ywvKf3nrSNH7xvUXzy852VIbvj694U7TWqkunfEOqlS+THa1PF7fWOxS1fkkvbXFevej9yIVBI/JyDTwLOnURoGAaAei/KC8xnbv/UYsJR9Ulp6vdtax+/N3NAG0vvlzqZnXNuv0vS5r8y9dWX1+43zIufQe3ownQ3or3/nVpORats5Z6Q0YA2lrvOEBPZuUR/LhzDdB25IdVxAv36y3jhfipiwAF0whAl/x4spgvP+fns5ceL+YfZ6vP7tFqt+nZRx2CHtY/37oK3z2u7Sye+yz/4fS93r8sabIk05PF6d1yd2/57xe/KAyuDayoVmnUXvGFzxeLr19bXdwZj1ap7dQB6PoQvrPe0UP4+gh+3LkGaCvykrzL7Sh+6lK9Lh7DT10EKJjGAFpgI/9QXyr/IefA8kNd/vBR67O8/P618tubANpdPPe5Xa2g9S+58e1y80q6FCtcfrlpO5oAXa24WrBacjRapY7TGEC76x0FaH3OdNx5DdBm5Pps8nr71htDTVUEKJhGAFp/eOtD99URePVZ7i5Wfn/5Ed8A0O7ia5/uv9R8KlddDw463rgdDYDWK654toL8aLT1GlpOYwDtrncMoOvtG3c+bP2OqCIf9Xc3A1/woyKIAAXTCEBbn+VFyYXmzzavRje+f7gBoL3Fa5/evyxpUu9irmjS3Esc244mQLsBylWORavVdhoFaHe9YwBdLz/uXAO0Ffk4v0L1+aKp9SqoqYoABdPoVfjy6+ZnPT/KXGsNmvVnv4OZNlV6i9c+vX/pcquNybHtaAK0vvRdLbj661i0zivQtBoCaG+9RgAdcV5fhW85rS40nX/r8ejGUdMTAQqmQACtzs1ND6BoW6FIAAACqklEQVS9nd5AAO3sA4cH6GqoU34Vvh7IRIBOXgQomCwBOngOznwPtL14E6Dtf9kK0JFzgbu0B5rr63sv5AitbmUiQCcvAhRMNgAdOwdnfg60vfgQ51bqA3Q9GOjShnOBXYAOnQPdBtCmU2ND2smczoHaA3SxaI6z4jnQ6YsABZMNQNfXjzuf5aMGdTZche8u3vRp/0uXJseN8U63x7ejD9CjevftOCuvwm8GaMep3tntJOuu1+gqvA1A+7+UeBVegwhQMFkBNB/c/aT8ujlFRj0uc+s40Nbia5/uv3QBWo/xqcejDm5HH6AD40A3A7TjVAP6uJ3MaRyo1R5oPYxp/TuC40CnLwIUTFYALe6/eW8xePvOS18Y3YnUXLzh3fmX3vHsce9OpMHt6AG0ecfQtYUBQAec8i+LaebvL1Zcmz/urXfDnUhrDloBdPn13q3l90/vNnZ2eSfS1EWAgskOoOt70Dsn48qbuy/8rxsB2lm86d3+l/4JwbF74dvb0Qdo/174LQDtOFV3+V/8uCb56kai9npHAbpMUt8Lb3cO9GRWZVwPceAp0KmLAAWTJUAXp+/m14XXUwRV+ro/G9MQVVqLt7xb/zJwRaU3G9PQdgwAtJw16erjjuUoQDtOqxmSbtUb8nD5j5eedNc7Pp1dazamQeexi0jzTy7nA0Erh3wNPIKfughQigqpkPOBhloTFU0EKEWFVLj9Rj4USYEIUIoKqlA7jtwB1SAClKLCKtCeI3dANYgApaiw2vZceDPxufAqRIBSVGCdzPxvIJrf5QG8BhGgFEVRjiJAKYqiHEWAUhRFOYoApSiKchQBSlEU5SgClKIoylEEKEVRlKMIUIqiKEcRoBRFUY4iQCmKohxFgFIURTmKAKUoinIUAUpRFOUoApSiKMpR/xVrC3/J67atCQAAAABJRU5ErkJggg==" width="672" /></p>
<div class="sourceCode" id="cb118"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb118-1"><a href="#cb118-1" tabindex="-1"></a>p3 <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rdd66<span class="sc">$</span>busi, df_rdd66<span class="sc">$</span>running_var2,</span>
<span id="cb118-2"><a href="#cb118-2" tabindex="-1"></a>             <span class="at">covs =</span> <span class="fu">cbind</span>(df_rdd66<span class="sc">$</span>age, df_rdd66<span class="sc">$</span>female, df_rdd66<span class="sc">$</span>income, df_rdd66<span class="sc">$</span>education, df_rdd66<span class="sc">$</span>psu_rul),</span>
<span id="cb118-3"><a href="#cb118-3" tabindex="-1"></a>             <span class="at">x.lim =</span> cutoff_range2, </span>
<span id="cb118-4"><a href="#cb118-4" tabindex="-1"></a>             <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb118-5"><a href="#cb118-5" tabindex="-1"></a>             <span class="at">y.lab =</span> <span class="st">&quot;Sociotropic economic evaluation&quot;</span>,</span>
<span id="cb118-6"><a href="#cb118-6" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Cutoff: The Gold medal confiramtion of women&#39;s judo 66kg&quot;</span>,</span>
<span id="cb118-7"><a href="#cb118-7" tabindex="-1"></a>             <span class="at">c =</span> <span class="dv">0</span>) </span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<div class="sourceCode" id="cb120"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb120-1"><a href="#cb120-1" tabindex="-1"></a>p4 <span class="ot">&lt;-</span> <span class="fu">rdplot</span>(df_rdd66<span class="sc">$</span>liv, df_rdd66<span class="sc">$</span>running_var2,</span>
<span id="cb120-2"><a href="#cb120-2" tabindex="-1"></a>             <span class="at">covs =</span> <span class="fu">cbind</span>(df_rdd66<span class="sc">$</span>age, df_rdd66<span class="sc">$</span>female, df_rdd66<span class="sc">$</span>income, df_rdd66<span class="sc">$</span>education, df_rdd66<span class="sc">$</span>psu_rul),</span>
<span id="cb120-3"><a href="#cb120-3" tabindex="-1"></a>             <span class="at">x.lim =</span> cutoff_range2, </span>
<span id="cb120-4"><a href="#cb120-4" tabindex="-1"></a>             <span class="at">x.lab =</span> <span class="st">&quot;Time difference from cutoff (mins)&quot;</span>,</span>
<span id="cb120-5"><a href="#cb120-5" tabindex="-1"></a>             <span class="at">y.lab =</span> <span class="st">&quot;Egotropic economic evaluation&quot;</span>,</span>
<span id="cb120-6"><a href="#cb120-6" tabindex="-1"></a>             <span class="at">title =</span> <span class="st">&quot;Cutoff: The Gold medal confiramtion of women&#39;s judo 66kg&quot;</span>,</span>
<span id="cb120-7"><a href="#cb120-7" tabindex="-1"></a>             <span class="at">c =</span> <span class="dv">0</span>) </span></code></pre></div>
<pre><code>## [1] &quot;covs option is meant to be used when plotting RDROBUST estimates. If the option is used for global polynomial fitting, it may deliver results that are not visually compatible with the local binned means depicted due to the underlying assumptions used.&quot;</code></pre>
<p><img role="img" 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" width="672" /></p>
<div class="sourceCode" id="cb122"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb122-1"><a href="#cb122-1" tabindex="-1"></a><span class="fu">grid.arrange</span>(p1<span class="sc">$</span>rdplot, p2<span class="sc">$</span>rdplot,</span>
<span id="cb122-2"><a href="#cb122-2" tabindex="-1"></a>             p3<span class="sc">$</span>rdplot, p4<span class="sc">$</span>rdplot,<span class="at">ncol =</span> <span class="dv">2</span>)</span></code></pre></div>
<p><img role="img" src="data:image/png;base64,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